documentation/classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml - platform/external/ComputeLibrary - Git at Google

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 <div class="header">
   <div class="summary">
 <a href="#pub-methods">Public Member Functions</a> &#124;
 <a href="#pub-static-methods">Static Public Member Functions</a>  </div>
   <div class="headertitle">
 <div class="title">CLLSTMLayerQuantized Class Reference</div>  </div>
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 <p>Basic function to run <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>.
  <a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#details">More...</a></p>

 <p><code>#include &lt;<a class="el" href="_c_l_l_s_t_m_layer_quantized_8h_source.xhtml">CLLSTMLayerQuantized.h</a>&gt;</code></p>
 <div class="dynheader">
 Collaboration diagram for CLLSTMLayerQuantized:</div>
 <div class="dyncontent">
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 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
 Public Member Functions</h2></td></tr>
 <tr class="memitem:a9e382561b8278103570de8550f83e718"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a9e382561b8278103570de8550f83e718">CLLSTMLayerQuantized</a> (std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt; memory_manager=nullptr)</td></tr>
 <tr class="memdesc:a9e382561b8278103570de8550f83e718"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor.  <a href="#a9e382561b8278103570de8550f83e718">More...</a><br /></td></tr>
 <tr class="separator:a9e382561b8278103570de8550f83e718"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:aab0c733ab7346ae4c2af856e167cad66"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aab0c733ab7346ae4c2af856e167cad66">CLLSTMLayerQuantized</a> (const <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;)=delete</td></tr>
 <tr class="memdesc:aab0c733ab7346ae4c2af856e167cad66"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#aab0c733ab7346ae4c2af856e167cad66">More...</a><br /></td></tr>
 <tr class="separator:aab0c733ab7346ae4c2af856e167cad66"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a9afd562eb46197912b68962e90c84185"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a9afd562eb46197912b68962e90c84185">CLLSTMLayerQuantized</a> (<a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:a9afd562eb46197912b68962e90c84185"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move constructor.  <a href="#a9afd562eb46197912b68962e90c84185">More...</a><br /></td></tr>
 <tr class="separator:a9afd562eb46197912b68962e90c84185"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a763bbaa72074a155be5385a583d0b9f3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a763bbaa72074a155be5385a583d0b9f3">operator=</a> (const <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a763bbaa72074a155be5385a583d0b9f3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a763bbaa72074a155be5385a583d0b9f3">More...</a><br /></td></tr>
 <tr class="separator:a763bbaa72074a155be5385a583d0b9f3"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a81d7e9fd8c62419c4c0b75617fe014d2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a81d7e9fd8c62419c4c0b75617fe014d2">operator=</a> (<a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:a81d7e9fd8c62419c4c0b75617fe014d2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move assignment operator.  <a href="#a81d7e9fd8c62419c4c0b75617fe014d2">More...</a><br /></td></tr>
 <tr class="separator:a81d7e9fd8c62419c4c0b75617fe014d2"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ab90e9ae19db4dbc4f316851b03402bfa"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">configure</a> (const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_input_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_forget_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_cell_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_output_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_input_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_forget_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_cell_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_output_weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_gate_bias, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *forget_gate_bias, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_bias, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_gate_bias, <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_state_in, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_in, <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_state_out, <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_out)</td></tr>
 <tr class="memdesc:ab90e9ae19db4dbc4f316851b03402bfa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize function's tensors.  <a href="#ab90e9ae19db4dbc4f316851b03402bfa">More...</a><br /></td></tr>
 <tr class="separator:ab90e9ae19db4dbc4f316851b03402bfa"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr>
 <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Run the kernels contained in the function.  <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr>
 <tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr>
 <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prepare the function for executing.  <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr>
 <tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr>
 <tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr>
 <tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor.  <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr>
 <tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
 </table><table class="memberdecls">
 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
 Static Public Member Functions</h2></td></tr>
 <tr class="memitem:aea08b58ba60ca803b947023ce07c5f79"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">validate</a> (const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_input_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_forget_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_cell_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_output_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_input_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_forget_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_cell_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_output_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_gate_bias, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *forget_gate_bias, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_bias, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_gate_bias, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_state_in, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_state_in, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_state_out, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_state_out)</td></tr>
 <tr class="memdesc:aea08b58ba60ca803b947023ce07c5f79"><td class="mdescLeft">&#160;</td><td class="mdescRight">Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>.  <a href="#aea08b58ba60ca803b947023ce07c5f79">More...</a><br /></td></tr>
 <tr class="separator:aea08b58ba60ca803b947023ce07c5f79"><td class="memSeparator" colspan="2">&#160;</td></tr>
 </table>
 <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
 <div class="textblock"><p>Basic function to run <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>. </p>
 <p>This function calls the following CL functions/kernels:</p>
 <ol type="1">
 <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml">CLGEMMLowpMatrixMultiplyCore</a> Quantized matrix multiplication core. Accumulators are 32-bit integers</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml">CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint</a> Convert 32-bit integers into QSYMM16</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_transpose.xhtml">CLTranspose</a> Matrix transpose</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml">CLConcatenateLayer</a> <a class="el" href="classarm__compute_1_1_tensor.xhtml" title="Basic implementation of the tensor interface.">Tensor</a> concatenation</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a> Activation functions (tanh and logistic)</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml">CLArithmeticAddition</a> Elementwise addition</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml">CLPixelWiseMultiplication</a> Elementwise multiplication</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_slice.xhtml">CLSlice</a> <a class="el" href="classarm__compute_1_1_tensor.xhtml" title="Basic implementation of the tensor interface.">Tensor</a> slicing</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_dequantization_layer.xhtml">CLDequantizationLayer</a> Dequantize into float</li>
 <li><a class="el" href="classarm__compute_1_1_c_l_quantization_layer.xhtml">CLQuantizationLayer</a> Quantize from float </li>
 </ol>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8h_source.xhtml#l00061">61</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8h_source.xhtml">CLLSTMLayerQuantized.h</a>.</p>
 </div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
 <a id="a9e382561b8278103570de8550f83e718"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a9e382561b8278103570de8550f83e718">&#9670;&nbsp;</a></span>CLLSTMLayerQuantized() <span class="overload">[1/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> </td>
           <td>(</td>
           <td class="paramtype">std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt;&#160;</td>
           <td class="paramname"><em>memory_manager</em> = <code>nullptr</code></td><td>)</td>
           <td></td>
         </tr>
       </table>
 </div><div class="memdoc">

 <p>Default constructor. </p>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    : _memory_group(std::move(memory_manager)), _gemmlowp(), _output_stage(), _transpose_weights(), _concat_input_weights(), _concat_recurrent_weights(), _concat_weights(), _concat_inputs(),</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;      _concat_bias(), _sigmoid_forget_gate(), _sigmoid_input_gate(), _sigmoid_output_gate(), _tanh_modulation_gate(), _tanh_output_state(), _add_cell_state_tmps(), _add2(), _mul_forget_gate_cell_state(),</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;      _mul_input_gate_input_mod_gate(), _mul_output_state_tmp_output_gate(), _slice_input_tensor(), _slice_forget_tensor(), _slice_cell_tensor(), _slice_output_tensor(), _dequantize(), _quantize(),</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;      _input_to_input_weights(<span class="keyword">nullptr</span>), _input_to_forget_weights(<span class="keyword">nullptr</span>), _input_to_cell_weights(<span class="keyword">nullptr</span>), _input_to_output_weights(<span class="keyword">nullptr</span>), _recurrent_to_input_weights(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;      _recurrent_to_forget_weights(<span class="keyword">nullptr</span>), _recurrent_to_cell_weights(<span class="keyword">nullptr</span>), _recurrent_to_output_weights(<span class="keyword">nullptr</span>), _input_gate_bias(<span class="keyword">nullptr</span>), _forget_gate_bias(<span class="keyword">nullptr</span>), _cell_bias(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;      _output_gate_bias(<span class="keyword">nullptr</span>), _recurrent_weights(), _input_weights(), _weights(), _input(), _weights_transposed(), _output_highp(), _output_lowp(), _bias(), _forget_gate_input(), _input_gate_input(),</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;      _output_gate_input(), _input_modulation_gate_input(), _forget_gate_output(), _input_gate_output(), _output_gate_output(), _input_modulation_gate_output(), _cell_state_tmp1(), _cell_state_tmp2(),</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;      _output_state_tmp(), _output_state_out_symm(), _output_state_out_f32(), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;{</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;}</div></div><!-- fragment -->
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 <h2 class="memtitle"><span class="permalink"><a href="#aab0c733ab7346ae4c2af856e167cad66">&#9670;&nbsp;</a></span>CLLSTMLayerQuantized() <span class="overload">[2/3]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
         </tr>
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   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">delete</span></span>  </td>
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 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

 </div>
 </div>
 <a id="a9afd562eb46197912b68962e90c84185"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a9afd562eb46197912b68962e90c84185">&#9670;&nbsp;</a></span>CLLSTMLayerQuantized() <span class="overload">[3/3]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
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 <p>Default move constructor. </p>

 </div>
 </div>
 <h2 class="groupheader">Member Function Documentation</h2>
 <a id="ab90e9ae19db4dbc4f316851b03402bfa"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ab90e9ae19db4dbc4f316851b03402bfa">&#9670;&nbsp;</a></span>configure()</h2>

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           <td class="memname">void configure </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
         </tr>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input_to_input_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input_to_forget_weights</em>, </td>
         </tr>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input_to_cell_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input_to_output_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_input_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_forget_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_cell_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_output_weights</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>input_gate_bias</em>, </td>
         </tr>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>forget_gate_bias</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>cell_bias</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>output_gate_bias</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>cell_state_in</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>output_state_in</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>cell_state_out</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *&#160;</td>
           <td class="paramname"><em>output_state_out</em>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
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 <p>Initialize function's tensors. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_input_weights</td><td>2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_forget_weights</td><td>2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_cell_weights</td><td>2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_output_weights</td><td>2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_input_weights</td><td>2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_forget_weights</td><td>2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_cell_weights</td><td>2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_output_weights</td><td>2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_gate_bias</td><td>1D weights tensor with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">forget_gate_bias</td><td>1D weights tensor with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">cell_bias</td><td>1D weights tensor with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">output_gate_bias</td><td>1D weights tensor with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">cell_state_in</td><td>2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">output_state_in</td><td>2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">cell_state_out</td><td>Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">output_state_out</td><td>Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as <code>input</code>. </td></tr>
   </table>
   </dd>
 </dl>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00058">58</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;{</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;                                 recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;                                 input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">CLLSTMLayerQuantized::validate</a>(input-&gt;info(), input_to_input_weights-&gt;info(), input_to_forget_weights-&gt;info(), input_to_cell_weights-&gt;info(),</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;                                                              input_to_output_weights-&gt;info(),</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;                                                              recurrent_to_input_weights-&gt;info(), recurrent_to_forget_weights-&gt;info(), recurrent_to_cell_weights-&gt;info(), recurrent_to_output_weights-&gt;info(),</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                                                              input_gate_bias-&gt;info(), forget_gate_bias-&gt;info(), cell_bias-&gt;info(), output_gate_bias-&gt;info(), cell_state_in-&gt;info(), output_state_in-&gt;info(), cell_state_out-&gt;info(), output_state_out-&gt;info()));</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> input_size  = input-&gt;info()-&gt;dimension(0);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size  = input-&gt;info()-&gt;dimension(1);</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> output_size = input_to_input_weights-&gt;info()-&gt;dimension(1);</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="keyword">const</span> QuantizationInfo qweights = input_to_input_weights-&gt;info()-&gt;quantization_info(); <span class="comment">// Weights quantization</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*cell_state_out-&gt;info(), TensorInfo(TensorShape(batch_size, output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_state_out-&gt;info(), TensorInfo(TensorShape(batch_size, output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm));</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    _input_to_input_weights      = input_to_input_weights;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    _input_to_forget_weights     = input_to_forget_weights;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    _input_to_cell_weights       = input_to_cell_weights;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    _input_to_output_weights     = input_to_output_weights;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    _recurrent_to_input_weights  = recurrent_to_input_weights;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    _recurrent_to_forget_weights = recurrent_to_forget_weights;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    _recurrent_to_cell_weights   = recurrent_to_cell_weights;</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    _recurrent_to_output_weights = recurrent_to_output_weights;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    _input_gate_bias             = input_gate_bias;</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    _forget_gate_bias            = forget_gate_bias;</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    _cell_bias                   = cell_bias;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    _output_gate_bias            = output_gate_bias;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="comment">// Weights concatenation</span></div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    std::vector&lt;const ICLTensor *&gt; inputs_weights_vector;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    inputs_weights_vector.emplace_back(input_to_input_weights);</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    inputs_weights_vector.emplace_back(input_to_forget_weights);</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    inputs_weights_vector.emplace_back(input_to_cell_weights);</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    inputs_weights_vector.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    std::vector&lt;const ICLTensor *&gt; recurrent_weights_vector;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_input_weights);</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_output_weights);</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    _input_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(TensorShape(input_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights));</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    _concat_input_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(inputs_weights_vector, &amp;_input_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    _recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(TensorShape(output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights));</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    _concat_recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(recurrent_weights_vector, &amp;_recurrent_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160; 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   _concat_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(weights_vector, &amp;_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    _transpose_weights.<a class="code" href="classarm__compute_1_1_c_l_transpose.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&amp;_weights, &amp;_weights_transposed);</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <span class="comment">// Input concatenation</span></div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    std::vector&lt;const ICLTensor *&gt; input_vector;</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    input_vector.emplace_back(input);</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    input_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input);</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    _input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(TensorShape(output_size + input_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm));</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    _concat_inputs.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(input_vector, &amp;_input, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160; 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   _concat_bias.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(bias_vector, &amp;_bias, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="comment">// Invert the offset for gemmlowp</span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    _input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset));</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    _weights_transposed.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <span class="comment">// Run gemmlowp</span></div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_highp);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    _output_highp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(TensorShape(4 * output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    _gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a0ae54876c8c3394f2e207f6b884f2b58">configure</a>(&amp;_input, &amp;_weights_transposed, <span class="keyword">nullptr</span>, &amp;_output_highp);</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    _input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    <span class="comment">// Set the offset back</span></div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    _input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset));</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    _weights_transposed.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160; 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   _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_lowp);</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    _output_stage.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#a5c7abeeecc0f2d6c50cc1dafaadf7c19">configure</a>(&amp;_output_highp, &amp;_bias, &amp;_output_lowp, output_multiplier, output_shift);</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    _output_highp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    _bias.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; 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       _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size });</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_modulation_gate_input);</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size });</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_gate_input);</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        _slice_output_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size });</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        _output_lowp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    }</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    {</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_gate_input);</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        _slice_input_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_input_gate_input, { 0 }, { output_size });</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_forget_gate_input);</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_forget_gate_input, { output_size }, { 2 * output_size });</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_modulation_gate_input);</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_input_modulation_gate_input, { 2 * output_size }, { 3 * output_size });</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_gate_input);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        _slice_output_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&amp;_output_lowp, &amp;_output_gate_input, { 3 * output_size }, { 4 * output_size });</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;        _output_lowp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    }</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    <span class="comment">// Forget gate</span></div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_forget_gate_output);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    _forget_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_forget_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    _sigmoid_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&amp;_forget_gate_input, &amp;_forget_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    _forget_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    <span class="comment">// Input gate</span></div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_gate_output);</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    _input_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_input_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    _sigmoid_input_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&amp;_input_gate_input, &amp;_input_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    _input_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <span class="comment">// Input modulation gate equation</span></div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_modulation_gate_output);</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    _input_modulation_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_input_modulation_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    _tanh_modulation_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&amp;_input_modulation_gate_input, &amp;_input_modulation_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f));</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    _input_modulation_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    <span class="comment">// Output gate</span></div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_gate_output);</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    _output_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_output_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    _sigmoid_output_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&amp;_output_gate_input, &amp;_output_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    _output_gate_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    <span class="comment">// Long term memory</span></div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_cell_state_tmp1);</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    _cell_state_tmp1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_forget_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    _mul_forget_gate_cell_state.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&amp;_forget_gate_output, cell_state_in, &amp;_cell_state_tmp1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    _forget_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_cell_state_tmp2);</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    _cell_state_tmp2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_input_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    _mul_input_gate_input_mod_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&amp;_input_gate_output, &amp;_input_modulation_gate_output, &amp;_cell_state_tmp2, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    _input_modulation_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    _input_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    _add_cell_state_tmps.<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">configure</a>(&amp;_cell_state_tmp1, &amp;_cell_state_tmp2, cell_state_out, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    _cell_state_tmp1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    _cell_state_tmp2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <span class="comment">// Short term memory</span></div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_state_tmp);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    _output_state_tmp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(cell_state_out-&gt;info()-&gt;tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    _tanh_output_state.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(cell_state_out, &amp;_output_state_tmp, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f));</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_state_out_symm);</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    _output_state_out_symm.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_output_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    _mul_output_state_tmp_output_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&amp;_output_state_tmp, &amp;_output_gate_output, &amp;_output_state_out_symm, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    _output_gate_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    _output_state_tmp.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    <span class="comment">// Requantize the output state from QSYMM16 to QASYMM8</span></div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_output_state_out_f32);</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    _output_state_out_f32.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(TensorInfo(_output_state_out_symm.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    _dequantize.<a class="code" href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&amp;_output_state_out_symm, &amp;_output_state_out_f32);</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    _output_state_out_symm.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    _quantize.<a class="code" href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&amp;_output_state_out_f32, output_state_out);</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    _output_state_out_f32.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a22032f9cf47deae265eafb65ff55b594"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(float multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00035">AsymmHelpers.cpp:35</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::DataType::QSYMM16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_arithmetic_addition_xhtml_a7af75ca77a6e9eb53532e7ab1317bdc3"><div class="ttname"><a href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">arm_compute::CLArithmeticAddition::configure</a></div><div class="ttdeci">void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)</div><div class="ttdoc">Initialise the kernel's inputs, output and conversion policy.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operations_8cpp_source.xhtml#l00050">CLElementwiseOperations.cpp:50</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_a0ae54876c8c3394f2e207f6b884f2b58"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a0ae54876c8c3394f2e207f6b884f2b58">arm_compute::CLGEMMLowpMatrixMultiplyCore::configure</a></div><div class="ttdeci">void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &amp;gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00075">CLGEMMLowpMatrixMultiplyCore.cpp:75</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point_xhtml_a5c7abeeecc0f2d6c50cc1dafaadf7c19"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#a5c7abeeecc0f2d6c50cc1dafaadf7c19">arm_compute::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min=0, int max=0)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00077">CLGEMMLowpOutputStage.cpp:77</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &amp;input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &amp;info, const TensorShape &amp;shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo &amp; set_quantization_info(const QuantizationInfo &amp;quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00364">TensorInfo.cpp:364</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
 <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_aea08b58ba60ca803b947023ce07c5f79"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">arm_compute::CLLSTMLayerQuantized::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLLSTMLayerQuantized.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00257">CLLSTMLayerQuantized.cpp:257</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div>
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 <div class="ttc" id="classarm__compute_1_1_c_l_pixel_wise_multiplication_xhtml_ab49eaee7b0a9443f7cbd3a73fd0bf2b2"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">arm_compute::CLPixelWiseMultiplication::configure</a></div><div class="ttdeci">void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)</div><div class="ttdoc">Initialise the kernel's inputs, output and convertion policy.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pixel_wise_multiplication_8cpp_source.xhtml#l00034">CLPixelWiseMultiplication.cpp:34</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic (  )</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_slice_xhtml_ae883a7cb96f6111b0e8bf3a64842c438"><div class="ttname"><a href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">arm_compute::CLSlice::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Coordinates &amp;starts, const Coordinates &amp;ends)</div><div class="ttdoc">Configure kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_slice_8cpp_source.xhtml#l00034">CLSlice.cpp:34</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_a6b12b22f2ad4a69582ec1674a2779ec8"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">arm_compute::CLConcatenateLayer::configure</a></div><div class="ttdeci">void configure(std::vector&lt; ICLTensor * &gt; &amp;inputs_vector, ICLTensor *output, size_t axis)</div><div class="ttdoc">Initialise the kernel's inputs vector and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00050">CLConcatenateLayer.cpp:50</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_dequantization_layer_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLDequantizationLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_dequantization_layer_8cpp_source.xhtml#l00031">CLDequantizationLayer.cpp:31</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">arm_compute::ActivationLayerInfo::ActivationFunction::TANH</a></div><div class="ttdoc">Hyperbolic tangent (  )</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_a239fea32ba46d038ba350dba58026c45"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">arm_compute::CLActivationLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00032">CLActivationLayer.cpp:32</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_transpose_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_transpose.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLTranspose::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Initialise the kernel's inputs and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_transpose_8cpp_source.xhtml#l00033">CLTranspose.cpp:33</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::ConvertPolicy::SATURATE</a></div><div class="ttdoc">Saturate.</div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape &amp; tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00252">TensorInfo.h:252</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06"><div class="ttname"><a href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">arm_compute::RoundingPolicy::TO_ZERO</a></div><div class="ttdoc">Truncates the least significant values that are lost in operations.</div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00327">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00201">arm_compute::auto_init_if_empty()</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00035">arm_compute::quantization::calculate_quantized_multiplier_less_than_one()</a>, <a class="el" href="_c_l_dequantization_layer_8cpp_source.xhtml#l00031">CLDequantizationLayer::configure()</a>, <a class="el" href="_c_l_transpose_8cpp_source.xhtml#l00033">CLTranspose::configure()</a>, <a class="el" href="_c_l_quantization_layer_8cpp_source.xhtml#l00031">CLQuantizationLayer::configure()</a>, <a class="el" href="_c_l_slice_8cpp_source.xhtml#l00034">CLSlice::configure()</a>, <a class="el" href="_c_l_elementwise_operations_8cpp_source.xhtml#l00050">CLArithmeticAddition::configure()</a>, <a class="el" href="_c_l_pixel_wise_multiplication_8cpp_source.xhtml#l00034">CLPixelWiseMultiplication::configure()</a>, <a class="el" href="_c_l_activation_layer_8cpp_source.xhtml#l00032">CLActivationLayer::configure()</a>, <a class="el" href="_c_l_concatenate_layer_8cpp_source.xhtml#l00050">CLConcatenateLayer::configure()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00075">CLGEMMLowpMatrixMultiplyCore::configure()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00077">CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor::info()</a>, <a class="el" href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator::init()</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::LOGISTIC</a>, <a class="el" href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase&lt; TensorType &gt;::manage()</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00062">UniformQuantizationInfo::offset</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::QSYMM16</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00061">UniformQuantizationInfo::scale</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00364">TensorInfo::set_quantization_info()</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::TANH</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00252">TensorInfo::tensor_shape()</a>, <a class="el" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">arm_compute::TO_ZERO</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo::uniform()</a>, and <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00257">CLLSTMLayerQuantized::validate()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00075">arm_compute::test::validation::TEST_CASE()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a763bbaa72074a155be5385a583d0b9f3">&#9670;&nbsp;</a></span>operator=() <span class="overload">[1/2]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>&amp; operator= </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
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 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a81d7e9fd8c62419c4c0b75617fe014d2">&#9670;&nbsp;</a></span>operator=() <span class="overload">[2/2]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>&amp; operator= </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
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 <p>Default move assignment operator. </p>

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 <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">&#9670;&nbsp;</a></span>prepare()</h2>

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           <td class="memname">void prepare </td>
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 <p>Prepare the function for executing. </p>
 <p>Any one off pre-processing step required by the function is handled here</p>
 <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl>

 <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00512">512</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;{</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    {</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        _input_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        _concat_input_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        _input_to_input_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;        _input_to_forget_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;        _input_to_cell_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        _input_to_output_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        _recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        _concat_recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;        _recurrent_to_input_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;        _recurrent_to_forget_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;        _recurrent_to_cell_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        _recurrent_to_output_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;        _weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        _concat_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;        _input_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        _input_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        _recurrent_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        _recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;        _weights_transposed.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;        _transpose_weights.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;        _weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;        _weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;        _bias.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;        _concat_bias.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        _input_gate_bias-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        _forget_gate_bias-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;        _cell_bias-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;        _output_gate_bias-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;        _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    }</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::CLTensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00152">CLTensorAllocator.cpp:152</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLConcatenateLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00243">CLConcatenateLayer.cpp:243</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00152">CLTensorAllocator::free()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction::run()</a>, and <a class="el" href="_c_l_concatenate_layer_8cpp_source.xhtml#l00243">CLConcatenateLayer::run()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00465">CLLSTMLayerQuantized::run()</a>.</p>

 </div>
 </div>
 <a id="ad1717410afd0be936c6213a63c8005fb"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">&#9670;&nbsp;</a></span>run()</h2>

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           <td class="memname">void run </td>
           <td>(</td>
           <td class="paramname"></td><td>)</td>
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 <p>Run the kernels contained in the function. </p>
 <p>For NEON kernels:</p><ul>
 <li>Multi-threading is used for the kernels which are parallelisable.</li>
 <li>By default std::thread::hardware_concurrency() threads are used.</li>
 </ul>
 <dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl>
 <p>For OpenCL kernels:</p><ul>
 <li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li>
 <li>The queue is then flushed.</li>
 </ul>
 <dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd>
 <dd>
 Will call <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl>

 <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00465">465</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;{</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    <span class="comment">// Acquire all the temporaries</span></div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    <span class="comment">// Concat and transpose the input</span></div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    _concat_inputs.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <span class="comment">// Run gemmlowp</span></div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    _gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    _output_stage.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    <span class="comment">// Slice the results</span></div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    _slice_input_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    _slice_output_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    <span class="comment">// Gates</span></div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    <span class="comment">// Forget gate</span></div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    _sigmoid_forget_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    <span class="comment">// Input gate</span></div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    _sigmoid_input_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;    <span class="comment">// Input modulation gate</span></div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    _tanh_modulation_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    <span class="comment">// Output gate</span></div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    _sigmoid_output_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    <span class="comment">// Cell state (long term memory)</span></div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    _mul_forget_gate_cell_state.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    _mul_input_gate_input_mod_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    _add_cell_state_tmps.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    <span class="comment">// Output state (short term memory)</span></div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    _tanh_output_state.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    _mul_output_state_tmp_output_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <span class="comment">// Requantize output state from QSYMM16 to QASYMM16</span></div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    _dequantize.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    _quantize.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLLSTMLayerQuantized::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00512">CLLSTMLayerQuantized.cpp:512</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMMLowpMatrixMultiplyCore::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00381">CLGEMMLowpMatrixMultiplyCore.cpp:381</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLConcatenateLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00243">CLConcatenateLayer.cpp:243</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00512">CLLSTMLayerQuantized::prepare()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction::run()</a>, <a class="el" href="_c_l_concatenate_layer_8cpp_source.xhtml#l00243">CLConcatenateLayer::run()</a>, and <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00381">CLGEMMLowpMatrixMultiplyCore::run()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00075">arm_compute::test::validation::TEST_CASE()</a>.</p>

 </div>
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 <a id="aea08b58ba60ca803b947023ce07c5f79"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#aea08b58ba60ca803b947023ce07c5f79">&#9670;&nbsp;</a></span>validate()</h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> validate </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input_to_input_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input_to_forget_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input_to_cell_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input_to_output_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_input_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_forget_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_cell_weights</em>, </td>
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           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>recurrent_to_output_weights</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input_gate_bias</em>, </td>
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           <td class="paramkey"></td>
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           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>forget_gate_bias</em>, </td>
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           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>cell_bias</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>output_gate_bias</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>cell_state_in</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>output_state_in</em>, </td>
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         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>cell_state_out</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>output_state_out</em>&#160;</td>
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           <td>)</td>
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 <span class="mlabels"><span class="mlabel">static</span></span>  </td>
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 <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml">CLLSTMLayerQuantized</a>. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_input_weights</td><td>2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_forget_weights</td><td>2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_cell_weights</td><td>2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_to_output_weights</td><td>2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_input_weights</td><td>2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_forget_weights</td><td>2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_cell_weights</td><td>2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_to_output_weights</td><td>2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">input_gate_bias</td><td>1D weights tensor info with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">forget_gate_bias</td><td>1D weights tensor info with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">cell_bias</td><td>1D weights tensor info with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">output_gate_bias</td><td>1D weights tensor info with dimensions [output_size]. Data type supported: S32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">cell_state_in</td><td>2D tensor info with dimensions [output_size, batch_size]. Data type supported: QSYMM16. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">output_state_in</td><td>2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">cell_state_out</td><td>Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size]. Data type supported: QSYMM16. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">output_state_out</td><td>Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as <code>input</code>.</td></tr>
   </table>
   </dd>
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 <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl>

 <p class="definition">Definition at line <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00257">257</a> of file <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;{</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;                                        recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in,</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;                                        output_state_in, cell_state_out, output_state_out);</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> input_size  = input-&gt;dimension(0);</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size  = input-&gt;dimension(1);</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> output_size = input_to_input_weights-&gt;dimension(1);</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    <span class="comment">// Dimensionality checks</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input-&gt;num_dimensions() &gt; 2);</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_to_input_weights-&gt;num_dimensions() &gt; 2);</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_gate_bias-&gt;num_dimensions() &gt; 1);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(output_state_in-&gt;num_dimensions() &gt; 2);</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    TensorInfo input_weights_info(input_to_input_weights-&gt;clone()-&gt;set_tensor_shape(TensorShape(input_size, output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>));</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    TensorInfo recurrent_weights_info(input_to_input_weights-&gt;clone()-&gt;set_tensor_shape(TensorShape(output_size, output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>));</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    TensorInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>(input_gate_bias-&gt;clone()-&gt;set_tensor_shape(TensorShape(output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    TensorInfo output_state_info(cell_state_in-&gt;clone()-&gt;set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>).set_quantization_info(qasymm));</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    TensorInfo cell_state_info(cell_state_in-&gt;clone()-&gt;set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>).set_quantization_info(qsymm_4));</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <span class="comment">// Shape checks</span></div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;cell_state_info, cell_state_in);</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;output_state_info, output_state_in);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <span class="comment">// Data type checks</span></div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;cell_state_info, cell_state_in);</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;output_state_info, output_state_in);</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    <span class="comment">// Quantization checks</span></div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&amp;cell_state_info, cell_state_in);</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&amp;output_state_info, output_state_in);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="comment">// Validate internal functions</span></div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <span class="comment">// _concat_input_weights</span></div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    std::vector&lt;const ITensorInfo *&gt; inputs_weights_vector;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    inputs_weights_vector.emplace_back(input_to_input_weights);</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    inputs_weights_vector.emplace_back(input_to_forget_weights);</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    inputs_weights_vector.emplace_back(input_to_cell_weights);</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    inputs_weights_vector.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keyword">const</span> QuantizationInfo qweights = input_to_input_weights-&gt;quantization_info(); <span class="comment">// Weights quantization</span></div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="keyword">const</span> TensorInfo       input_weights(TensorShape(input_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(inputs_weights_vector, &amp;input_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>));</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="comment">// _concat_recurrent_weights</span></div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    std::vector&lt;const ITensorInfo *&gt; recurrent_weights_vector;</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_input_weights);</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    recurrent_weights_vector.emplace_back(recurrent_to_output_weights);</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keyword">const</span> TensorInfo recurrent_weights(TensorShape(output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(recurrent_weights_vector, &amp;recurrent_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>));</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="comment">// _concat_weights</span></div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    std::vector&lt;const ITensorInfo *&gt; weights_vector;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    weights_vector.emplace_back(&amp;recurrent_weights);</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    weights_vector.emplace_back(&amp;input_weights);</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    <span class="keyword">const</span> TensorInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>(TensorShape(input_size + output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(weights_vector, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <span class="comment">// _transpose_weights</span></div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keyword">const</span> TensorShape weights_transposed_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape()[1], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape()[0]);</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    TensorInfo        weights_transposed = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.clone()-&gt;set_is_resizable(<span class="keyword">true</span>).set_tensor_shape(weights_transposed_shape);</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_transpose.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLTranspose::validate</a>(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;weights_transposed));</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="comment">// _concat_inputs</span></div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    std::vector&lt;const ITensorInfo *&gt; input_vector;</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    input_vector.emplace_back(input);</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    input_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    TensorInfo input_concatenated(TensorShape(output_size + input_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm);</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(input_vector, &amp;input_concatenated, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <span class="comment">// _concat_bias</span></div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    std::vector&lt;const ITensorInfo *&gt; bias_vector;</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;    bias_vector.emplace_back(input_gate_bias);</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    bias_vector.emplace_back(forget_gate_bias);</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    bias_vector.emplace_back(cell_bias);</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;    bias_vector.emplace_back(output_gate_bias);</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    <span class="keyword">const</span> TensorInfo bias_concatenated(TensorShape(4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(bias_vector, &amp;bias_concatenated, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="comment">// Invert the offset for gemmlowp</span></div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset));</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    <span class="comment">// _gemmlowp</span></div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    <span class="keyword">const</span> TensorInfo output_highp(TensorShape(4 * output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">CLGEMMLowpMatrixMultiplyCore::validate</a>(&amp;input_concatenated, &amp;weights_transposed, <span class="keyword">nullptr</span>, &amp;output_highp));</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    <span class="comment">// Set the offset back</span></div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset));</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="comment">// multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12))</span></div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <span class="keyword">const</span> TensorInfo output_lowp(output_highp.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(multiplier);</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(multiplier &gt; 1.0f);</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    <span class="comment">// _output_stage</span></div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate</a>(&amp;output_highp, &amp;bias_concatenated, &amp;output_lowp));</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    TensorInfo input_gate_input;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    TensorInfo forget_gate_input;</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    TensorInfo input_modulation_gate_input;</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    TensorInfo output_gate_input;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keywordflow">if</span>(batch_size &gt; 1)</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    {</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        <span class="comment">// _slice_input_tensor</span></div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        input_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;input_gate_input, { 0, 0 }, { output_size, batch_size }));</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        <span class="comment">// _slice_forget_tensor</span></div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;        forget_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }));</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;        <span class="comment">// _slice_cell_tensor</span></div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;        input_modulation_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }));</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;        <span class="comment">// _slice_output_tensor</span></div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        output_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }));</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    }</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    {</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;        <span class="comment">// _slice_input_tensor</span></div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;        input_gate_input = TensorInfo(TensorShape(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;input_gate_input, { 0 }, { output_size }));</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;        <span class="comment">// _slice_forget_tensor</span></div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        forget_gate_input = TensorInfo(TensorShape(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;forget_gate_input, { output_size }, { 2 * output_size }));</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;        <span class="comment">// _slice_cell_tensor</span></div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        input_modulation_gate_input = TensorInfo(TensorShape(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;input_modulation_gate_input, { 2 * output_size }, { 3 * output_size }));</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;        <span class="comment">// _slice_output_tensor</span></div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        output_gate_input = TensorInfo(TensorShape(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&amp;output_lowp, &amp;output_gate_input, { 3 * output_size }, { 4 * output_size }));</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    }</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <span class="comment">// _sigmoid_forget_gate</span></div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <span class="keyword">const</span> TensorInfo forget_gate_output(forget_gate_input.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&amp;forget_gate_input, &amp;forget_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <span class="comment">// _sigmoid_input_gate</span></div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <span class="keyword">const</span> TensorInfo input_gate_output(input_gate_input.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&amp;input_gate_input, &amp;input_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    <span class="comment">// _tanh_modulation_gate</span></div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;    <span class="keyword">const</span> TensorInfo input_modulation_gate_output(input_modulation_gate_input.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&amp;input_modulation_gate_input, &amp;input_modulation_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f)));</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;    <span class="comment">// _sigmoid_output_gate</span></div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="keyword">const</span> TensorInfo output_gate_output(output_gate_input.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&amp;output_gate_input, &amp;output_gate_output, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    <span class="comment">// _mul_forget_gate_cell_state</span></div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;    <span class="keyword">const</span> TensorInfo cell_state_tmp1(forget_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4);</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&amp;forget_gate_output, cell_state_in, &amp;cell_state_tmp1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    <span class="comment">// _mul_input_gate_input_mod_gate</span></div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    <span class="keyword">const</span> TensorInfo cell_state_tmp2(input_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4);</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&amp;input_gate_output, &amp;input_modulation_gate_output, &amp;cell_state_tmp2, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="comment">// _add_cell_state_tmps</span></div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&amp;cell_state_tmp1, &amp;cell_state_tmp2, cell_state_out, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    <span class="comment">// _tanh_modulation_gate</span></div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    <span class="keyword">const</span> TensorInfo output_state_tmp(cell_state_out-&gt;tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(cell_state_out, &amp;output_state_tmp, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f)));</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    <span class="comment">// _mul_output_state_tmp_output_gate</span></div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    <span class="keyword">const</span> TensorInfo output_state_out_symm(output_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&amp;output_state_tmp, &amp;output_gate_output, &amp;output_state_out_symm, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    <span class="comment">// _dequantize</span></div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    <span class="keyword">const</span> TensorInfo output_state_out_f32(output_state_out_symm.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLDequantizationLayer::validate</a>(&amp;output_state_out_symm, &amp;output_state_out_f32));</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    <span class="comment">// _quantize</span></div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLQuantizationLayer::validate</a>(&amp;output_state_out_f32, output_state_out));</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;    <span class="keywordflow">if</span>(cell_state_out-&gt;total_size() != 0)</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    {</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;cell_state_info, cell_state_out);</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;cell_state_info, cell_state_out);</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&amp;cell_state_info, cell_state_out);</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    }</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    <span class="keywordflow">if</span>(output_state_out-&gt;total_size() != 0)</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    {</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&amp;output_state_info, output_state_out);</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&amp;output_state_info, output_state_out);</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&amp;output_state_info, output_state_out);</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    }</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_c_l_arithmetic_addition_xhtml_a5e951bf3e414ddcd908245bcf284b08f"><div class="ttname"><a href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">arm_compute::CLArithmeticAddition::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSaturatedArithmeticOpe...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operations_8cpp_source.xhtml#l00058">CLElementwiseOperations.cpp:58</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::CLActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &amp;act_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00039">CLActivationLayer.cpp:39</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_dequantization_layer_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLDequantizationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDequantizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_dequantization_layer_8cpp_source.xhtml#l00038">CLDequantizationLayer.cpp:38</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::DataType::QSYMM16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number</div></div>
 <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point_xhtml_aee63e7671cf04d15be2da1b83d90e61b"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">arm_compute::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min=0, int max=0)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMLowpQuantizeDownIn...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">CLGEMMLowpOutputStage.cpp:86</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
 <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_a8c3cf2d65afb288e39909171ada19566"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">arm_compute::CLGEMMLowpMatrixMultiplyCore::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &amp;gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMLowpMatrixMultiply...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00237">CLGEMMLowpMatrixMultiplyCore.cpp:237</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_pixel_wise_multiplication_xhtml_a705182fc799dce8ee017368eea0ca539"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#a705182fc799dce8ee017368eea0ca539">arm_compute::CLPixelWiseMultiplication::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLPixelWiseMultiplicatio...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pixel_wise_multiplication_8cpp_source.xhtml#l00052">CLPixelWiseMultiplication.cpp:52</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_a27e4638546c88b8916f967e6e54480a9"><div class="ttname"><a href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00443">Validate.h:443</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a43c38c81ff3058e36ceb9904a944d1ea"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">arm_compute::test::validation::bias_info</a></div><div class="ttdeci">bias_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00469">Winograd.cpp:469</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_slice_xhtml_afb48914092f4bb2fdf249fa1e3fcd17c"><div class="ttname"><a href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">arm_compute::CLSlice::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &amp;starts, const Coordinates &amp;ends)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSlice.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_slice_8cpp_source.xhtml#l00046">CLSlice.cpp:46</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_a6e77b7a36830679af4f991604feab114"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">arm_compute::CLConcatenateLayer::validate</a></div><div class="ttdeci">static Status validate(const std::vector&lt; ITensorInfo * &gt; &amp;inputs_vector, const ITensorInfo *output, size_t axis)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLConcatenateLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00060">CLConcatenateLayer.cpp:60</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic (  )</div></div>
 <div class="ttc" id="_validate_8h_xhtml_aba910b683652be1f65437ef37a9da2a9"><div class="ttname"><a href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00610">Validate.h:610</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_transpose_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_transpose.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLTranspose::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLTranspose.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_transpose_8cpp_source.xhtml#l00040">CLTranspose.cpp:40</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">arm_compute::ActivationLayerInfo::ActivationFunction::TANH</a></div><div class="ttdoc">Hyperbolic tangent (  )</div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::ConvertPolicy::SATURATE</a></div><div class="ttdoc">Saturate.</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06"><div class="ttname"><a href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">arm_compute::RoundingPolicy::TO_ZERO</a></div><div class="ttdoc">Truncates the least significant values that are lost in operations.</div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_quantization_layer_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLQuantizationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLQuantizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_quantization_layer_8cpp_source.xhtml#l00038">CLQuantizationLayer.cpp:38</a></div></div>
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 <p class="reference">References <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00545">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_validate_8h_source.xhtml#l00610">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>, <a class="el" href="_validate_8h_source.xhtml#l00443">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00193">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00160">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00469">arm_compute::test::validation::bias_info</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::LOGISTIC</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00062">UniformQuantizationInfo::offset</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::QSYMM16</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00061">UniformQuantizationInfo::scale</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a575d37eaf8a905c8ca3c0250757c2b81">ITensorInfo::set_quantization_info()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00364">TensorInfo::set_quantization_info()</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::TANH</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00252">TensorInfo::tensor_shape()</a>, <a class="el" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">arm_compute::TO_ZERO</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">ITensorInfo::total_size()</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo::uniform()</a>, <a class="el" href="_c_l_dequantization_layer_8cpp_source.xhtml#l00038">CLDequantizationLayer::validate()</a>, <a class="el" href="_c_l_transpose_8cpp_source.xhtml#l00040">CLTranspose::validate()</a>, <a class="el" href="_c_l_quantization_layer_8cpp_source.xhtml#l00038">CLQuantizationLayer::validate()</a>, <a class="el" href="_c_l_activation_layer_8cpp_source.xhtml#l00039">CLActivationLayer::validate()</a>, <a class="el" href="_c_l_elementwise_operations_8cpp_source.xhtml#l00058">CLArithmeticAddition::validate()</a>, <a class="el" href="_c_l_pixel_wise_multiplication_8cpp_source.xhtml#l00052">CLPixelWiseMultiplication::validate()</a>, <a class="el" href="_c_l_slice_8cpp_source.xhtml#l00046">CLSlice::validate()</a>, <a class="el" href="_c_l_concatenate_layer_8cpp_source.xhtml#l00060">CLConcatenateLayer::validate()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00237">CLGEMMLowpMatrixMultiplyCore::validate()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate()</a>, and <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00058">CLLSTMLayerQuantized::configure()</a>.</p>

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 <hr/>The documentation for this class was generated from the following files:<ul>
 <li>arm_compute/runtime/CL/functions/<a class="el" href="_c_l_l_s_t_m_layer_quantized_8h_source.xhtml">CLLSTMLayerQuantized.h</a></li>
 <li>src/runtime/CL/functions/<a class="el" href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml">CLLSTMLayerQuantized.cpp</a></li>
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