documentation/classarm__compute_1_1_n_e_g_e_m_m.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">NEGEMM Class Reference</div>  </div>
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 <div class="contents">

 <p>Basic function to execute GEMM on NEON.
  <a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#details">More...</a></p>

 <p><code>#include &lt;<a class="el" href="_n_e_g_e_m_m_8h_source.xhtml">NEGEMM.h</a>&gt;</code></p>
 <div class="dynheader">
 Collaboration diagram for NEGEMM:</div>
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 <table class="memberdecls">
 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
 Public Member Functions</h2></td></tr>
 <tr class="memitem:abfe2d5e45f4d74fc2c76524d1c886199"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#abfe2d5e45f4d74fc2c76524d1c886199">NEGEMM</a> (std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt; memory_manager=nullptr, <a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *weights_manager=nullptr)</td></tr>
 <tr class="memdesc:abfe2d5e45f4d74fc2c76524d1c886199"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor.  <a href="#abfe2d5e45f4d74fc2c76524d1c886199">More...</a><br /></td></tr>
 <tr class="separator:abfe2d5e45f4d74fc2c76524d1c886199"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a545e2af1971b9885b46f0f58b5893628"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a545e2af1971b9885b46f0f58b5893628">NEGEMM</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a545e2af1971b9885b46f0f58b5893628"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a545e2af1971b9885b46f0f58b5893628">More...</a><br /></td></tr>
 <tr class="separator:a545e2af1971b9885b46f0f58b5893628"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ac2d5f41869fc7a6313f453067c09c882"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ac2d5f41869fc7a6313f453067c09c882">NEGEMM</a> (<a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:ac2d5f41869fc7a6313f453067c09c882"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move constructor.  <a href="#ac2d5f41869fc7a6313f453067c09c882">More...</a><br /></td></tr>
 <tr class="separator:ac2d5f41869fc7a6313f453067c09c882"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a01cad4311af868f78ee3de9e09b4a6a8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a01cad4311af868f78ee3de9e09b4a6a8">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a01cad4311af868f78ee3de9e09b4a6a8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a01cad4311af868f78ee3de9e09b4a6a8">More...</a><br /></td></tr>
 <tr class="separator:a01cad4311af868f78ee3de9e09b4a6a8"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a296a65f1339d5d42de2fd545a9dc6c6d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a296a65f1339d5d42de2fd545a9dc6c6d">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:a296a65f1339d5d42de2fd545a9dc6c6d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move assignment operator.  <a href="#a296a65f1339d5d42de2fd545a9dc6c6d">More...</a><br /></td></tr>
 <tr class="separator:a296a65f1339d5d42de2fd545a9dc6c6d"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a385241dcc5062af6ecac8bdafe01bb2a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *a, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *b, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *c, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *d, float alpha, float beta, const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> &amp;gemm_info=<a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>())</td></tr>
 <tr class="memdesc:a385241dcc5062af6ecac8bdafe01bb2a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialise the kernel's inputs, output.  <a href="#a385241dcc5062af6ecac8bdafe01bb2a">More...</a><br /></td></tr>
 <tr class="separator:a385241dcc5062af6ecac8bdafe01bb2a"><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_n_e_g_e_m_m.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_n_e_g_e_m_m.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:a3493ba7d1f2057740ff5931fa00a44ac"><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_n_e_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">validate</a> (const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *a, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *b, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *c, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, float alpha, float beta, const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> &amp;gemm_info=<a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>())</td></tr>
 <tr class="memdesc:a3493ba7d1f2057740ff5931fa00a44ac"><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_n_e_g_e_m_m.xhtml">NEGEMM</a>.  <a href="#a3493ba7d1f2057740ff5931fa00a44ac">More...</a><br /></td></tr>
 <tr class="separator:a3493ba7d1f2057740ff5931fa00a44ac"><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 execute GEMM on NEON. </p>
 <p>This function calls the following NEON kernels:</p>
 <p>If optimized assembly is available:</p><ol type="1">
 <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml">NEGEMMAssemblyDispatch</a></li>
 <li><a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a> (if alpha != 1.0) Else:</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml">NEGEMMInterleave4x4Kernel</a> (if the output tensor is a matrix)</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml">NEGEMMTranspose1xWKernel</a> (if the output tensor is a matrix)</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml">NEGEMMMatrixMultiplyKernel</a> In both cases:</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml">NEGEMMMatrixAdditionKernel</a> (if c != nullptr and beta != 0.0 and is not reshaped once) Else:</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml">NEArithmeticAdditionKernel</a> (if c != nullptr and is reshaped once and not optimized assembly in place)</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a> (if activation is specified in <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml" title="GEMM information class.">GEMMInfo</a>) </li>
 </ol>

 <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_8h_source.xhtml#l00059">59</a> of file <a class="el" href="_n_e_g_e_m_m_8h_source.xhtml">NEGEMM.h</a>.</p>
 </div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
 <a id="abfe2d5e45f4d74fc2c76524d1c886199"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#abfe2d5e45f4d74fc2c76524d1c886199">&#9670;&nbsp;</a></span>NEGEMM() <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_n_e_g_e_m_m.xhtml">NEGEMM</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>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *&#160;</td>
           <td class="paramname"><em>weights_manager</em> = <code>nullptr</code>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
       </table>
 </div><div class="memdoc">

 <p>Constructor. </p>

 <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00044">44</a> of file <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    : _memory_group(memory_manager), _weights_manager(weights_manager), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _asm_glue(memory_manager, weights_manager), _ma_kernel(),</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;      _alpha_scale_func(<span class="keyword">nullptr</span>), _add_bias_kernel(), _activation_func(), _tmp_a(), _tmp_b(), _tmp_d(), _original_b(<span class="keyword">nullptr</span>), _run_vector_matrix_multiplication(<span class="keyword">false</span>), _run_alpha_scale(<span class="keyword">false</span>),</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;      _run_addition(<span class="keyword">false</span>), _run_bias_addition(<span class="keyword">false</span>), _run_activation(<span class="keyword">false</span>), _reshape_b_only_on_first_run(<span class="keyword">false</span>), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;{</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;}</div></div><!-- fragment -->
 </div>
 </div>
 <a id="a545e2af1971b9885b46f0f58b5893628"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a545e2af1971b9885b46f0f58b5893628">&#9670;&nbsp;</a></span>NEGEMM() <span class="overload">[2/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
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   <tr>
   <td class="mlabels-left">
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         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
         </tr>
       </table>
   </td>
   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">delete</span></span>  </td>
   </tr>
 </table>
 </div><div class="memdoc">

 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

 </div>
 </div>
 <a id="ac2d5f41869fc7a6313f453067c09c882"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ac2d5f41869fc7a6313f453067c09c882">&#9670;&nbsp;</a></span>NEGEMM() <span class="overload">[3/3]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
         </tr>
       </table>
   </td>
   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">default</span></span>  </td>
   </tr>
 </table>
 </div><div class="memdoc">

 <p>Default move constructor. </p>

 </div>
 </div>
 <h2 class="groupheader">Member Function Documentation</h2>
 <a id="a385241dcc5062af6ecac8bdafe01bb2a"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a385241dcc5062af6ecac8bdafe01bb2a">&#9670;&nbsp;</a></span>configure()</h2>

 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname">void configure </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>a</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>b</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>c</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>d</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">float&#160;</td>
           <td class="paramname"><em>alpha</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">float&#160;</td>
           <td class="paramname"><em>beta</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> &amp;&#160;</td>
           <td class="paramname"><em>gemm_info</em> = <code><a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>()</code>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
       </table>
 </div><div class="memdoc">

 <p>Initialise the kernel's inputs, output. </p>
 <dl class="section note"><dt>Note</dt><dd>GEMM: General Matrix Multiply - [alpha * A * B + beta * C]. </dd>
 <dd>
 GEMM: The tensors a, b, c, d must have the same data type. You should not mix data types when calling this function.</dd></dl>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">a</td><td>First input tensor (Matrix A or <a class="el" href="struct_vector.xhtml" title="Structure to hold Vector information.">Vector</a> A). Data type supported: F16/F32 </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">b</td><td>Second input tensor (Matrix B). Data type supported: same as <code>a</code> </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">c</td><td>Third input tensor (Matrix C). It can be a nullptr if just the multiplication between <code>a</code> and <code>b</code> is needed. Data type supported: same as <code>a</code> </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">d</td><td>Output tensor. Data type supported: same as <code>a</code> </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">alpha</td><td>Weight of the matrix product </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">beta</td><td>Weight of matrix C </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">gemm_info</td><td>(Optional) Specifies if the matrix A and/or matrix B have been reshaped and if the reshape of matrix B should happen only for the first run </td></tr>
   </table>
   </dd>
 </dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">51</a> of file <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;{</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">NEGEMM::validate</a>(a-&gt;info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info(), (c != <span class="keyword">nullptr</span>) ? c-&gt;info() : <span class="keyword">nullptr</span>, d-&gt;info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, beta, gemm_info));</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> is_c_bias     = gemm_info.reshape_b_only_on_first_run();</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keywordtype">bool</span>       run_optimised = bool(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a0dbb699477c9a3cc78f530462c55447c">NEGEMMAssemblyDispatch::validate</a>(a-&gt;info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info(), (is_c_bias &amp;&amp; c != <span class="keyword">nullptr</span>) ? c-&gt;info() : <span class="keyword">nullptr</span>, d-&gt;info(), gemm_info));</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="comment">// Check if we need to reshape the matrix B only on the first run</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    _is_prepared                      = <span class="keyword">false</span>;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    _reshape_b_only_on_first_run      = gemm_info.reshape_b_only_on_first_run();</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    _run_vector_matrix_multiplication = a-&gt;info()-&gt;dimension(1) &lt; 2;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    _original_b                       = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    _run_alpha_scale                  = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a> != 1.f;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    _run_bias_addition                = c != <span class="keyword">nullptr</span> &amp;&amp; gemm_info.reshape_b_only_on_first_run();</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    _run_addition                     = beta != 0 &amp;&amp; c != <span class="keyword">nullptr</span> &amp;&amp; !gemm_info.reshape_b_only_on_first_run();</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    _run_activation                   = gemm_info.activation_info().enabled() &amp;&amp; (!run_optimised || (run_optimised &amp;&amp; !<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a15eae37f4cc172cf75bf9bc64b840ce5">NEGEMMAssemblyDispatch::is_activation_supported</a>(gemm_info.activation_info())));</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="keywordflow">if</span>(run_optimised)</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    {</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        <span class="keyword">const</span> ITensor *c_to_use = is_c_bias ? c : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        <span class="keywordflow">if</span>(<a class="code" href="classarm__compute_1_1_m_e_m_info.xhtml#aacc842e7ef6389e970d92af1d62793a7">MEMInfo::get_policy</a>() == <a class="code" href="namespacearm__compute.xhtml#a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda">MemoryPolicy::MINIMIZE</a>)</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;        {</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;            GEMMInfo gemm_info_ntb = gemm_info;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;            gemm_info_ntb.set_pretranpose_B(<span class="keyword">false</span>);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;            _asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a46b97f2afe13f54a9f5b6ff9017271a7">configure</a>(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, c_to_use, d, gemm_info_ntb);</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;        }</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        {</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;            _asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a46b97f2afe13f54a9f5b6ff9017271a7">configure</a>(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, c_to_use, d, gemm_info);</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        }</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">is_configured</a>());</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;        <span class="comment">// Scale product by alpha</span></div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;        <span class="keywordflow">if</span>(_run_alpha_scale)</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        {</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            _alpha_scale_func.<a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">configure</a>(d, <span class="keyword">nullptr</span>, ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::ActivationFunction::LINEAR</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, 0.f));</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        }</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    }</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    {</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="comment">// Pick output tensor in case bias addition should be performed</span></div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        ITensor *gemm_output_to_use = d;</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        <span class="keywordflow">if</span>(_run_bias_addition)</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        {</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;            gemm_output_to_use = &amp;_tmp_d;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;            _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_tmp_d);</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        }</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        <span class="comment">// Select between GEMV and GEMM</span></div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        <span class="keywordflow">if</span>(_run_vector_matrix_multiplication)</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        {</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;            <span class="comment">// Configure the matrix multiply kernel</span></div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;            _mm_kernel.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml#a4ee07709711414457834cc5d1c2c6cdb">configure</a>(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, gemm_output_to_use, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, <span class="keyword">false</span>);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        }</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        {</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;            TensorShape shape_tmp_a = a-&gt;info()-&gt;tensor_shape();</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;            TensorShape shape_tmp_b = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;tensor_shape();</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;            shape_tmp_a.set(0, a-&gt;info()-&gt;dimension(0) * 4);</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;            shape_tmp_a.set(1, std::ceil(a-&gt;info()-&gt;dimension(1) / 4.0f));</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> transpose_w = 16 / <a class="code" href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">data_size_from_type</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;data_type());</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;            shape_tmp_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;dimension(1) * transpose_w);</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;            shape_tmp_b.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;dimension(0) / static_cast&lt;float&gt;(transpose_w)));</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;            TensorInfo info_a = a-&gt;info()-&gt;clone()-&gt;set_tensor_shape(shape_tmp_a).set_is_resizable(<span class="keyword">true</span>);</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;            TensorInfo info_b = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;clone()-&gt;set_tensor_shape(shape_tmp_b).set_is_resizable(<span class="keyword">true</span>);</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;            _tmp_a.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(info_a);</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;            _tmp_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(info_b);</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">// Manage intermediate buffers</span></div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;            _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_tmp_a);</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            <span class="keywordflow">if</span>(!_reshape_b_only_on_first_run)</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;            {</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;                _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_tmp_b);</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            }</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;            <span class="keywordtype">int</span> m = a-&gt;info()-&gt;dimension(1);</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;            <span class="keywordtype">int</span> n = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;info()-&gt;dimension(0);</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;            <span class="keywordtype">int</span> k = a-&gt;info()-&gt;dimension(0);</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;            <span class="comment">// Configure interleave kernel</span></div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;            _interleave_kernel.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(a, &amp;_tmp_a);</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; 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           }</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        }</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        <span class="keywordflow">if</span>(_run_bias_addition)</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        {</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;            _add_bias_kernel.<a class="code" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#ae549ed675eab6d763ac6ffd18d226c27">configure</a>(gemm_output_to_use, c, d, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;            _tmp_d.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</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;    }</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="comment">// Configure matrix addition kernel</span></div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordflow">if</span>(_run_addition)</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    {</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        _ma_kernel.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml#abd3df2f9251266926538e4c020652996">configure</a>(c, d, beta);</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    }</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    <span class="comment">// Configure activation</span></div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <span class="keyword">const</span> ActivationLayerInfo &amp;activation = gemm_info.activation_info();</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="keywordflow">if</span>(_run_activation)</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    {</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        _activation_func.<a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">configure</a>(d, <span class="keyword">nullptr</span>, activation);</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;}</div><div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &amp;allocator, const Coordinates &amp;coords, TensorInfo &amp;sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aa76b4a6e74940dabc5b7fc6b2dab3545"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">arm_compute::test::validation::b</a></div><div class="ttdeci">SimpleTensor&lt; float &gt; b</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">DFT.cpp:157</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_arithmetic_addition_kernel_xhtml_ae549ed675eab6d763ac6ffd18d226c27"><div class="ttname"><a href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#ae549ed675eab6d763ac6ffd18d226c27">arm_compute::NEArithmeticAdditionKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy)</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00915">NEArithmeticAdditionKernel.cpp:915</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#l00455">Error.h:455</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda"><div class="ttname"><a href="namespacearm__compute.xhtml#a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda">arm_compute::MemoryPolicy::MINIMIZE</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</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_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_a3493ba7d1f2057740ff5931fa00a44ac"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">arm_compute::NEGEMM::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &amp;gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMM.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00172">NEGEMM.cpp:172</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_a15eae37f4cc172cf75bf9bc64b840ce5"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a15eae37f4cc172cf75bf9bc64b840ce5">arm_compute::NEGEMMAssemblyDispatch::is_activation_supported</a></div><div class="ttdeci">static bool is_activation_supported(const ActivationLayerInfo &amp;activation)</div><div class="ttdoc">Checks if activation is supported by the gemm assembly dispatcher.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00535">NEGEMMAssemblyDispatch.cpp:535</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_ab7c16a89cb470f3fa85818ee85e1e1dd"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">arm_compute::NEGEMMAssemblyDispatch::is_configured</a></div><div class="ttdeci">bool is_configured() const</div><div class="ttdoc">Was the function successfully configured ?</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch.cpp:597</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEGEMMInterleave4x4Kernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_interleave4x4_kernel_8cpp_source.xhtml#l00178">NEGEMMInterleave4x4Kernel.cpp:178</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_abb7e0f23a4f2e63f39433f158dad47ab"><div class="ttname"><a href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">arm_compute::data_size_from_type</a></div><div class="ttdeci">size_t data_size_from_type(DataType data_type)</div><div class="ttdoc">The size in bytes of the data type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00109">Utils.h:109</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_m_e_m_info_xhtml_aacc842e7ef6389e970d92af1d62793a7"><div class="ttname"><a href="classarm__compute_1_1_m_e_m_info.xhtml#aacc842e7ef6389e970d92af1d62793a7">arm_compute::MEMInfo::get_policy</a></div><div class="ttdeci">static MemoryPolicy get_policy()</div><div class="ttdef"><b>Definition:</b> <a href="_m_e_m_utils_8cpp_source.xhtml#l00086">MEMUtils.cpp:86</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3161c2c93c655dd30953372064ec627b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">arm_compute::test::validation::alpha</a></div><div class="ttdeci">const float alpha</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">Accumulate.cpp:103</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEGEMMTranspose1xWKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_transpose1x_w_kernel_8cpp_source.xhtml#l00095">NEGEMMTranspose1xWKernel.cpp:95</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_a0dbb699477c9a3cc78f530462c55447c"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a0dbb699477c9a3cc78f530462c55447c">arm_compute::NEGEMMAssemblyDispatch::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, const GEMMInfo &amp;gemm_info)</div><div class="ttdoc">Indicates whether or not this function can be used to process the given parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00506">NEGEMMAssemblyDispatch.cpp:506</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel_xhtml_a4ee07709711414457834cc5d1c2c6cdb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml#a4ee07709711414457834cc5d1c2c6cdb">arm_compute::NEGEMMMatrixMultiplyKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &amp;reshape_info=GEMMReshapeInfo())</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_multiply_kernel_8cpp_source.xhtml#l00962">NEGEMMMatrixMultiplyKernel.cpp:962</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">arm_compute::ActivationLayerInfo::ActivationFunction::LINEAR</a></div><div class="ttdoc">Linear (  )</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_xhtml_adfb5ef37594fc9371c4a2b95e3d5e31b"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">arm_compute::NEActivationLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer.cpp:36</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel_xhtml_abd3df2f9251266926538e4c020652996"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml#abd3df2f9251266926538e4c020652996">arm_compute::NEGEMMMatrixAdditionKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, float beta)</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_addition_kernel_8cpp_source.xhtml#l00116">NEGEMMMatrixAdditionKernel.cpp:116</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_a46b97f2afe13f54a9f5b6ff9017271a7"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a46b97f2afe13f54a9f5b6ff9017271a7">arm_compute::NEGEMMAssemblyDispatch::configure</a></div><div class="ttdeci">void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, const GEMMInfo &amp;gemm_info)</div><div class="ttdoc">If supported create an ACL function else fallback to the arm_gemm function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00541">NEGEMMAssemblyDispatch.cpp:541</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>
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 <p class="reference">References <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02134">GEMMInfo::activation_info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">arm_compute::test::validation::alpha</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">arm_compute::test::validation::b</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="_n_e_g_e_m_m_matrix_addition_kernel_8cpp_source.xhtml#l00116">NEGEMMMatrixAdditionKernel::configure()</a>, <a class="el" href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer::configure()</a>, <a class="el" href="_n_e_g_e_m_m_interleave4x4_kernel_8cpp_source.xhtml#l00178">NEGEMMInterleave4x4Kernel::configure()</a>, <a class="el" href="_n_e_g_e_m_m_matrix_multiply_kernel_8cpp_source.xhtml#l00962">NEGEMMMatrixMultiplyKernel::configure()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00541">NEGEMMAssemblyDispatch::configure()</a>, <a class="el" href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00915">NEArithmeticAdditionKernel::configure()</a>, <a class="el" href="_n_e_g_e_m_m_transpose1x_w_kernel_8cpp_source.xhtml#l00095">NEGEMMTranspose1xWKernel::configure()</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l00109">arm_compute::data_size_from_type()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01664">ActivationLayerInfo::enabled()</a>, <a class="el" href="_m_e_m_utils_8cpp_source.xhtml#l00086">MEMInfo::get_policy()</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00535">NEGEMMAssemblyDispatch::is_activation_supported()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch::is_configured()</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::LINEAR</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda">arm_compute::MINIMIZE</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02054">GEMMInfo::reshape_b_only_on_first_run()</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02126">GEMMInfo::set_pretranpose_B()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00506">NEGEMMAssemblyDispatch::validate()</a>, and <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00172">NEGEMM::validate()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00067">NERNNLayer::configure()</a>, <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00270">NEWinogradConvolutionLayer::configure()</a>, and <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00056">NELSTMLayer::configure()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a01cad4311af868f78ee3de9e09b4a6a8">&#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_n_e_g_e_m_m.xhtml">NEGEMM</a>&amp; operator= </td>
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           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> &amp;&#160;</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="#a296a65f1339d5d42de2fd545a9dc6c6d">&#9670;&nbsp;</a></span>operator=() <span class="overload">[2/2]</span></h2>

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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="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">335</a> of file <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;{</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    {</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;        <span class="keywordflow">if</span>(_asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">is_configured</a>())</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        {</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            <span class="keywordflow">if</span>(!_weights_manager || !_weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_b))</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            {</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_b-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            }</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;            _asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;        }</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(_reshape_b_only_on_first_run &amp;&amp; !_run_vector_matrix_multiplication &amp;&amp; !_asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">is_configured</a>())</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        {</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;            <span class="keywordflow">if</span>(!_weights_manager || !_weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_b))</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;                <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_b-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;            }</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;            _tmp_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;            <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_transpose_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;            _original_b-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;        }</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;        _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    }</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</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_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMMAssemblyDispatch::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Runs a preparation step, usually for pre-transposing matrix b.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00591">NEGEMMAssemblyDispatch.cpp:591</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_ab7c16a89cb470f3fa85818ee85e1e1dd"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">arm_compute::NEGEMMAssemblyDispatch::is_configured</a></div><div class="ttdeci">bool is_configured() const</div><div class="ttdoc">Was the function successfully configured ?</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch.cpp:597</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</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_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &amp;hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div>
 <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div>
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 <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch::is_configured()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00591">NEGEMMAssemblyDispatch::prepare()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00120">NERNNLayer::prepare()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer::prepare()</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00563">NEGEMMConvolutionLayer::prepare()</a>, and <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM::run()</a>.</p>

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

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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_n_e_g_e_m_m.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="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">285</a> of file <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;{</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    MemoryGroupResourceScope scope_mg(_memory_group);</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="keywordflow">if</span>(_asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">is_configured</a>())</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    {</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;        _asm_glue.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <span class="keywordflow">if</span>(_run_alpha_scale)</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        {</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;            _alpha_scale_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</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;    }</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    {</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        <span class="keywordflow">if</span>(!_run_vector_matrix_multiplication)</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        {</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;            <span class="comment">// Run interleave kernel</span></div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;            <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_interleave_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            <span class="keywordflow">if</span>(!_reshape_b_only_on_first_run)</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;            {</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;                <span class="comment">// Run transpose kernel</span></div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;                <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_transpose_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;            }</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;        }</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_mm_kernel, _run_vector_matrix_multiplication ? <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a> : <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">// Run bias addition kernel</span></div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        <span class="keywordflow">if</span>(_run_bias_addition)</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;        {</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;            <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_add_bias_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        }</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    }</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="comment">// Run matrix addition kernel</span></div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="keywordflow">if</span>(_run_addition)</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    {</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;        <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_ma_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    }</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    <span class="comment">// Run activation function</span></div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="keywordflow">if</span>(_run_activation)</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    {</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;        _activation_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    }</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::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_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMMAssemblyDispatch::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="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00602">NEGEMMAssemblyDispatch.cpp:602</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_n_e_g_e_m_m_assembly_dispatch_xhtml_ab7c16a89cb470f3fa85818ee85e1e1dd"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#ab7c16a89cb470f3fa85818ee85e1e1dd">arm_compute::NEGEMMAssemblyDispatch::is_configured</a></div><div class="ttdeci">bool is_configured() const</div><div class="ttdoc">Was the function successfully configured ?</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch.cpp:597</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_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &amp;hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMM::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="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM.cpp:335</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <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="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00597">NEGEMMAssemblyDispatch::is_configured()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM::prepare()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00602">NEGEMMAssemblyDispatch::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00552">NEWinogradConvolutionLayer::run()</a>, <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00103">NERNNLayer::run()</a>, <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00611">NELSTMLayer::run()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00381">NEFullyConnectedLayer::run()</a>, and <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00524">NEGEMMConvolutionLayer::run()</a>.</p>

 </div>
 </div>
 <a id="a3493ba7d1f2057740ff5931fa00a44ac"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a3493ba7d1f2057740ff5931fa00a44ac">&#9670;&nbsp;</a></span>validate()</h2>

 <div class="memitem">
 <div class="memproto">
 <table class="mlabels">
   <tr>
   <td class="mlabels-left">
       <table class="memname">
         <tr>
           <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>a</em>, </td>
         </tr>
         <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>b</em>, </td>
         </tr>
         <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>c</em>, </td>
         </tr>
         <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>output</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">float&#160;</td>
           <td class="paramname"><em>alpha</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">float&#160;</td>
           <td class="paramname"><em>beta</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> &amp;&#160;</td>
           <td class="paramname"><em>gemm_info</em> = <code><a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>()</code>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
       </table>
   </td>
   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">static</span></span>  </td>
   </tr>
 </table>
 </div><div class="memdoc">

 <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a>. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">a</td><td>First input tensor info (Matrix or <a class="el" href="struct_vector.xhtml" title="Structure to hold Vector information.">Vector</a> A). Data types supported: F16/F32 </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">b</td><td>Second input tensor info (Matrix B). Data type supported: same as <code>a</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">c</td><td>Third input tensor info (Matrix C). It can be a nullptr if just the multiplication between <code>a</code> and <code>b</code> is needed. Data type supported: same as <code>a</code>. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Output tensor info. Data type supported: same as <code>a</code> </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">alpha</td><td>Weight of the matrix product </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">beta</td><td>Weight of matrix C </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">gemm_info</td><td>(Optional) Specifies if the matrix A and/or matrix B have been reshaped and if the reshape of matrix B should happen only for the first run</td></tr>
   </table>
   </dd>
 </dl>
 <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00172">172</a> of file <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;{</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>);</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> is_c_bias = gemm_info.reshape_b_only_on_first_run();</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <a class="code" href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a>(a);</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(a, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, output);</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(a-&gt;dimension(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;dimension(1), <span class="stringliteral">&quot;The product AB is defined only if the number of columns in A is equal to the number of rows in B&quot;</span>);</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(gemm_info.is_a_reshaped(), <span class="stringliteral">&quot;Matrix A already reshaped is not supported&quot;</span>);</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(gemm_info.is_b_reshaped(), <span class="stringliteral">&quot;Matrix B already reshaped is not supported&quot;</span>);</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <span class="keywordflow">if</span>(c != <span class="keyword">nullptr</span> &amp;&amp; !is_c_bias)</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    {</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(gemm_info.depth_output_gemm3d() != 0);</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(gemm_info.reinterpret_input_as_3d());</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(a, c);</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(a-&gt;dimension(1) != c-&gt;dimension(1), <span class="stringliteral">&quot;The C matrix must have the same number of rows as the matrix A&quot;</span>);</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;dimension(0) != c-&gt;dimension(0), <span class="stringliteral">&quot;The C matrix must have the same number of columns as the matrix B&quot;</span>);</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    }</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <span class="keywordflow">if</span>(output-&gt;total_size() != 0)</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;        <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;dimension(0) != output-&gt;dimension(0));</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        <span class="keywordflow">if</span>(gemm_info.depth_output_gemm3d() != 0)</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        {</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;            <span class="keywordflow">if</span>(gemm_info.reinterpret_input_as_3d())</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;            {</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;                <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(a-&gt;dimension(1) != output-&gt;dimension(1));</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(a-&gt;dimension(2) != output-&gt;dimension(2));</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;            }</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;            <span class="keywordflow">else</span></div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;            {</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;                <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(a-&gt;dimension(1) != output-&gt;dimension(1) * output-&gt;dimension(2));</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;            }</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="keywordflow">else</span></div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        {</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;            <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(a-&gt;dimension(1) != output-&gt;dimension(1));</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        }</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    }</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">// Check if we need to run the optimized assembly kernel</span></div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> run_optimised = bool(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a0dbb699477c9a3cc78f530462c55447c">NEGEMMAssemblyDispatch::validate</a>(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, is_c_bias ? c : <span class="keyword">nullptr</span>, output, gemm_info));</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <span class="keywordflow">if</span>(!run_optimised)</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    {</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(gemm_info.reinterpret_input_as_3d(), <span class="stringliteral">&quot;NEGEMM cannot reinterpret the input tensor as 3D&quot;</span>);</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(gemm_info.depth_output_gemm3d() != 0, <span class="stringliteral">&quot;NEGEMM cannot reinterpret the output tensor as 3D&quot;</span>);</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        <span class="comment">// Check if the first input tensor is a vector.</span></div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">bool</span> run_vector_matrix_multiplication = a-&gt;dimension(1) &lt; 2;</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        <span class="comment">// Check if we need to reshape the matrix A and matrix B</span></div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">bool</span> run_interleave_transpose = !run_vector_matrix_multiplication &amp;&amp; !(gemm_info.reshape_b_only_on_first_run());</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;        <span class="comment">// Arguments used by GEMMReshapeInfo</span></div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;        <span class="comment">// If we pass the matrix A and matrix B reshaped to NEGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to NEGEMMReshapeInfo</span></div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        <span class="comment">// in order to know how the matrices have been reshaped</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> m                         = a-&gt;dimension(1);</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> n                         = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;dimension(0);</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> k                         = a-&gt;dimension(0);</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        <span class="keywordtype">int</span>       mult_transpose1xW_width   = 1;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <span class="keywordtype">int</span>       mult_interleave4x4_height = 1;</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="keyword">const</span> GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, gemm_info.depth_output_gemm3d());</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        <span class="keyword">const</span> ITensorInfo *matrix_a_info = a;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        <span class="keyword">const</span> ITensorInfo *matrix_b_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>;</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;        TensorInfo tmp_a_info{};</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        TensorInfo tmp_b_info{};</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        TensorInfo tmp_output_info = *output-&gt;clone();</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        <span class="keywordflow">if</span>(run_interleave_transpose)</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;            matrix_a_info = &amp;tmp_a_info;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;            matrix_b_info = &amp;tmp_b_info;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;            <span class="comment">// Validate interleave kernel</span></div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;            <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(tmp_a_info, a-&gt;clone()-&gt;set_tensor_shape(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb">compute_interleaved_shape</a>(*a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d())));</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;            <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEGEMMInterleave4x4Kernel::validate</a>(a, &amp;tmp_a_info));</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            <span class="comment">// Validate transpose kernel</span></div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;            <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(tmp_b_info, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>-&gt;clone()-&gt;set_tensor_shape(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">compute_transpose1xW_with_element_size_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, mult_transpose1xW_width)));</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;            <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEGEMMTranspose1xWKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, &amp;tmp_b_info));</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        }</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        <span class="comment">// Validate matrix multiply</span></div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(tmp_output_info, matrix_a_info-&gt;clone()-&gt;set_tensor_shape(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">compute_mm_shape</a>(*matrix_a_info, *matrix_b_info, run_interleave_transpose, reshape_info)));</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml#ac91476ef04db46dad52f3cd1e2fae18e">NEGEMMMatrixMultiplyKernel::validate</a>(matrix_a_info, matrix_b_info, &amp;tmp_output_info, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, run_interleave_transpose, reshape_info));</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;        <span class="keywordflow">if</span>(c != <span class="keyword">nullptr</span> &amp;&amp; gemm_info.reshape_b_only_on_first_run())</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        {</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;            <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#a5e951bf3e414ddcd908245bcf284b08f">NEArithmeticAdditionKernel::validate</a>(&amp;tmp_output_info, c, output, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        }</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;</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="comment">// Validate matrix addition kernel</span></div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="keywordflow">if</span>(beta != 0 &amp;&amp; c != <span class="keyword">nullptr</span> &amp;&amp; !is_c_bias)</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;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml#ae722d2edbab88977ea4604d5395d8463">NEGEMMMatrixAdditionKernel::validate</a>(c, output, beta));</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    }</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="comment">// Validate activation</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keyword">const</span> ActivationLayerInfo &amp;activation = gemm_info.activation_info();</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keywordflow">if</span>(activation.enabled())</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    {</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, activation));</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    }</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5797726a8fbee3b11b92757c2f0031d6"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">arm_compute::misc::shape_calculator::compute_transpose1xW_with_element_size_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInfo &amp;b, int mult_transpose1xW_width=1)</div><div class="ttdoc">Calculate the transposed 1xW width element shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00337">ShapeCalculator.h:337</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_arithmetic_addition_kernel_xhtml_a5e951bf3e414ddcd908245bcf284b08f"><div class="ttname"><a href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#a5e951bf3e414ddcd908245bcf284b08f">arm_compute::NEArithmeticAdditionKernel::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 NEArithmeticAdditionKern...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00971">NEArithmeticAdditionKernel.cpp:971</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aa76b4a6e74940dabc5b7fc6b2dab3545"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">arm_compute::test::validation::b</a></div><div class="ttdeci">SimpleTensor&lt; float &gt; b</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">DFT.cpp:157</a></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="_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#l00204">Error.h:204</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_adca241b012a5e00ddfcdc5a8db05a2a3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">arm_compute::misc::shape_calculator::compute_mm_shape</a></div><div class="ttdeci">TensorShape compute_mm_shape(const ITensorInfo &amp;input0, const ITensorInfo &amp;input1, bool is_interleaved_transposed, const GEMMReshapeInfo &amp;reshape_info)</div><div class="ttdoc">Calculate the matrix multiplication output shape of two tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00858">ShapeCalculator.h:858</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::NEActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &amp;act_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00043">NEActivationLayer.cpp:43</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00792">Validate.h:792</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#l00296">Error.h:296</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel_xhtml_ae722d2edbab88977ea4604d5395d8463"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml#ae722d2edbab88977ea4604d5395d8463">arm_compute::NEGEMMMatrixAdditionKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMMatrixAdditionKern...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_addition_kernel_8cpp_source.xhtml#l00144">NEGEMMMatrixAdditionKernel.cpp:144</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8d52adbbcd2c53f837c96b5a3d15c4fb"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb">arm_compute::misc::shape_calculator::compute_interleaved_shape</a></div><div class="ttdeci">TensorShape compute_interleaved_shape(const ITensorInfo &amp;a, int mult_interleave4x4_height=1, bool reinterpret_input_as_3d=false)</div><div class="ttdoc">Calculate the interleaved shape of an input tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00260">ShapeCalculator.h:260</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEGEMMTranspose1xWKernel::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 NEGEMMTranspose1xWKernel...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_transpose1x_w_kernel_8cpp_source.xhtml#l00114">NEGEMMTranspose1xWKernel.cpp:114</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#l00202">Helpers.inl:202</a></div></div>
 <div class="ttc" id="_c_p_p_2_validate_8h_xhtml_ad2633f3560322e1f8d926949dec1b730"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_validate_8h_source.xhtml#l00071">Validate.h:71</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</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#l00152">Error.h:152</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEGEMMInterleave4x4Kernel::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 NEGEMMInterleave4x4Kerne...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_interleave4x4_kernel_8cpp_source.xhtml#l00213">NEGEMMInterleave4x4Kernel.cpp:213</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3161c2c93c655dd30953372064ec627b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">arm_compute::test::validation::alpha</a></div><div class="ttdeci">const float alpha</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">Accumulate.cpp:103</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch_xhtml_a0dbb699477c9a3cc78f530462c55447c"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml#a0dbb699477c9a3cc78f530462c55447c">arm_compute::NEGEMMAssemblyDispatch::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, const GEMMInfo &amp;gemm_info)</div><div class="ttdoc">Indicates whether or not this function can be used to process the given parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00506">NEGEMMAssemblyDispatch.cpp:506</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</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="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel_xhtml_ac91476ef04db46dad52f3cd1e2fae18e"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml#ac91476ef04db46dad52f3cd1e2fae18e">arm_compute::NEGEMMMatrixMultiplyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &amp;reshape_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMMatrixMultiplyKern...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_multiply_kernel_8cpp_source.xhtml#l00987">NEGEMMMatrixMultiplyKernel.cpp:987</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><!-- fragment -->
 <p class="reference">References <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02134">GEMMInfo::activation_info()</a>, <a class="el" href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">arm_compute::test::validation::alpha</a>, <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_c_p_p_2_validate_8h_source.xhtml#l00071">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a>, <a class="el" href="_validate_8h_source.xhtml#l00792">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_validate_8h_source.xhtml#l00545">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00152">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00202">arm_compute::auto_init_if_empty()</a>, <a class="el" href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">arm_compute::test::validation::b</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="src_2core_2_tensor_info_8cpp_source.xhtml#l00314">TensorInfo::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00260">arm_compute::misc::shape_calculator::compute_interleaved_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00858">arm_compute::misc::shape_calculator::compute_mm_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00337">arm_compute::misc::shape_calculator::compute_transpose1xW_with_element_size_shape()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02062">GEMMInfo::depth_output_gemm3d()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01664">ActivationLayerInfo::enabled()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02036">GEMMInfo::is_a_reshaped()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02044">GEMMInfo::is_b_reshaped()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02070">GEMMInfo::reinterpret_input_as_3d()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02054">GEMMInfo::reshape_b_only_on_first_run()</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">ITensorInfo::total_size()</a>, <a class="el" href="_n_e_g_e_m_m_interleave4x4_kernel_8cpp_source.xhtml#l00213">NEGEMMInterleave4x4Kernel::validate()</a>, <a class="el" href="_n_e_g_e_m_m_matrix_addition_kernel_8cpp_source.xhtml#l00144">NEGEMMMatrixAdditionKernel::validate()</a>, <a class="el" href="_n_e_activation_layer_8cpp_source.xhtml#l00043">NEActivationLayer::validate()</a>, <a class="el" href="_n_e_g_e_m_m_matrix_multiply_kernel_8cpp_source.xhtml#l00987">NEGEMMMatrixMultiplyKernel::validate()</a>, <a class="el" href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00971">NEArithmeticAdditionKernel::validate()</a>, <a class="el" href="_n_e_g_e_m_m_assembly_dispatch_8cpp_source.xhtml#l00506">NEGEMMAssemblyDispatch::validate()</a>, and <a class="el" href="_n_e_g_e_m_m_transpose1x_w_kernel_8cpp_source.xhtml#l00114">NEGEMMTranspose1xWKernel::validate()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM::configure()</a>, and <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00381">NELSTMLayer::validate()</a>.</p>

 </div>
 </div>
 <hr/>The documentation for this class was generated from the following files:<ul>
 <li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_g_e_m_m_8h_source.xhtml">NEGEMM.h</a></li>
 <li>src/runtime/NEON/functions/<a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml">NEGEMM.cpp</a></li>
 </ul>
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