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<div class="title">GEMMLowp.cpp</div> </div>
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<a href="validation_2reference_2_g_e_m_m_lowp_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_g_e_m_m_lowp_8h.xhtml">GEMMLowp.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_utils_quantized_asymm_8h.xhtml">tests/validation/reference/UtilsQuantizedAsymm.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &lt;limits&gt;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;{</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;{</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">namespace </span>validation</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">namespace </span>reference</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;<span class="keywordtype">void</span> quantize_down_int32_to_uint8_scale(<span class="keyword">const</span> SimpleTensor&lt;T&gt; *in, <span class="keyword">const</span> SimpleTensor&lt;T&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, SimpleTensor&lt;uint8_t&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; int32_t min, int32_t max)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> cols_in = in-&gt;shape().x();</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; in-&gt;num_elements(); ++i)</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; int32_t result = ((*in)[i] + result_offset);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> != <span class="keyword">nullptr</span>)</div><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; result += (*bias)[i % cols_in];</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;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; result *= result_mult_int;</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; result &gt;&gt;= result_shift;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="comment">// Bounded ReLu</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="keywordflow">if</span>(min != max)</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; result = std::max(min, std::min(max, result));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; (*dst)[i] = static_cast&lt;uint8_t&gt;(std::max(0, std::min(255, result)));</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;}</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">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;<span class="keywordtype">void</span> quantize_down_int32_to_uint8_scale_by_fixedpoint(<span class="keyword">const</span> SimpleTensor&lt;T&gt; *in, <span class="keyword">const</span> SimpleTensor&lt;T&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, SimpleTensor&lt;uint8_t&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; int32_t result_offset_after_shift, int32_t min, int32_t max)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;{</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> cols_in = in-&gt;shape().x();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; in-&gt;num_elements(); ++i)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; int32_t result = (*in)[i];</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; result += (*bias)[i % cols_in];</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="comment">// Fixed point multiplication</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; result = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">asymm_rounding_divide_by_pow2</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">asymm_int_mult</a>(result, result_fixedpoint_multiplier), result_shift);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; result += result_offset_after_shift;</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="comment">// Bounded ReLu</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="keywordflow">if</span>(min != max)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; result = std::max(min, std::min(max, result));</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; }</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; (*dst)[i] = static_cast&lt;uint8_t&gt;(std::max(0, std::min(255, result)));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; }</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="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;<span class="keywordtype">void</span> quantize_down_int32_to_int16_scale_by_fixedpoint(<span class="keyword">const</span> SimpleTensor&lt;T&gt; *in, <span class="keyword">const</span> SimpleTensor&lt;T&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, SimpleTensor&lt;int16_t&gt; *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; int32_t min, int32_t max)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;{</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> cols_in = in-&gt;shape().x();</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">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; in-&gt;num_elements(); ++i)</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; int32_t result = (*in)[i];</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; result += (*bias)[i % cols_in];</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;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="comment">// Fixed point multiplication</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; result = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">asymm_rounding_divide_by_pow2</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">asymm_int_mult</a>(result, result_fixedpoint_multiplier), result_shift);</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; <span class="comment">// Bounded ReLu</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <span class="keywordflow">if</span>(min != max)</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; result = std::max(min, std::min(max, result));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; }</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; (*dst)[i] = static_cast&lt;int16_t&gt;(std::max(-32768, std::min(32767, result)));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160;}</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T_out, <span class="keyword">typename</span> T_in&gt;</div><div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a74776be88be65092fe631a3313f46ab5"> 129</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T_out&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a74776be88be65092fe631a3313f46ab5">gemmlowp_matrix_multiply_core</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T_in&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T_in&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c, int32_t a_offset, int32_t b_offset)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160;{</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; static_assert(std::is_same&lt;<span class="keyword">typename</span> std::decay&lt;T_out&gt;::type, int32_t&gt;::value, <span class="stringliteral">&quot;Only int32_t is allowed for the output&quot;</span>);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dt = std::is_same&lt;T_out, int32_t&gt;::value ? <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a> : <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T_out&gt;</a> c(shape_c, dt);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> K = a.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> M = a.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</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>.shape().x();</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> D = a.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().z(); <span class="comment">// Number of matrices in a batch</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> a_stride_z = K * M;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="comment">// Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> b_stride_z = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.shape().num_dimensions() &gt; 2 ? N * K : 0;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> c_stride_z = N * M;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; std::vector&lt;T_out&gt; acc;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; acc.resize(N);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> depth = 0; depth &lt; D; ++depth)</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="keyword">const</span> <span class="keywordtype">int</span> base_addr_a = depth * a_stride_z;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> base_addr_b = depth * b_stride_z;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> base_addr_c = depth * c_stride_z;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; M; ++i)</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; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j &lt; N; ++j)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; acc[j] = 0;</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; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k &lt; K; ++k)</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; <span class="keyword">const</span> T_out tmp_a = a_offset + static_cast&lt;T_out&gt;(a[base_addr_a + k + i * K]);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j &lt; N; ++j)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="keyword">const</span> T_out tmp_b = b_offset + static_cast&lt;T_out&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>[base_addr_b + j + k * N]);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <span class="keyword">const</span> T_out mult_as_int = tmp_a * tmp_b;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; acc[j] += mult_as_int;</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="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j &lt; N; ++j)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; c[base_addr_c + j + i * N] = acc[j];</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; }</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;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <span class="keywordflow">return</span> c;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;}</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;<span class="comment">// used to validate assembly kernels which don&#39;t know anything about offsets</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T1, <span class="keyword">typename</span> T2&gt;</div><div class="line"><a name="l00183"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#af18f21173e19d772347089a2839351b8"> 183</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T1&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#af18f21173e19d772347089a2839351b8">gemmlowp</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T2&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T2&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;{</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keywordflow">return</span> gemmlowp_matrix_multiply_core&lt;T1, T2&gt;(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, shape_c, 0, 0);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;}</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00189"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606"> 189</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606">gemmlowp_quantize_down_int32_to_uint8_scale</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160;{</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</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; quantize_down_int32_to_uint8_scale&lt;T&gt;(&amp;in, <span class="keyword">nullptr</span>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_offset, result_mult_int, result_shift, min, max);</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; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160;}</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="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00199"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a32a9b5d6bd895eb4a431ed762a6891c1"> 199</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606">gemmlowp_quantize_down_int32_to_uint8_scale</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; int32_t min, int32_t max)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;{</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; quantize_down_int32_to_uint8_scale&lt;T&gt;(&amp;in, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_offset, result_mult_int, result_shift, min, max);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160;}</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105"> 210</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105">gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; int32_t result_offset_after_shift, int32_t min,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; int32_t max)</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; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; quantize_down_int32_to_uint8_scale_by_fixedpoint&lt;T&gt;(&amp;in, <span class="keyword">nullptr</span>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160;}</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00222"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a43a9895cb024f65fd294a76b51a16092"> 222</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105">gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; int32_t result_offset_after_shift, int32_t min, int32_t max)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160;{</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</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; quantize_down_int32_to_uint8_scale_by_fixedpoint&lt;T&gt;(&amp;in, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160;}</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb"> 233</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb">gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; int32_t max)</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; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>);</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; quantize_down_int32_to_int16_scale_by_fixedpoint&lt;T&gt;(&amp;in, <span class="keyword">nullptr</span>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_fixedpoint_multiplier, result_shift, min, max);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;}</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00244"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ac64d1c55d1ebc90fe55a46c35682e759"> 244</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb">gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; int32_t min, int32_t max)</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; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; quantize_down_int32_to_int16_scale_by_fixedpoint&lt;T&gt;(&amp;in, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, result_fixedpoint_multiplier, result_shift, min, max);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160;}</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105">gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; int32_t result_offset_after_shift, int32_t min, int32_t max);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105">gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, int32_t result_fixedpoint_multiplier,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb">gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, int32_t result_fixedpoint_multiplier, int32_t result_shift,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; int32_t min, int32_t max);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb">gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, int32_t result_fixedpoint_multiplier,</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; int32_t result_shift, int32_t min, int32_t max);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606">gemmlowp_quantize_down_int32_to_uint8_scale</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; int32_t max);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606">gemmlowp_quantize_down_int32_to_uint8_scale</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, int32_t result_offset, int32_t result_mult_int,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; int32_t result_shift, int32_t min, int32_t max);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a74776be88be65092fe631a3313f46ab5">gemmlowp_matrix_multiply_core</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int8_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int8_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c, int32_t a_offset, int32_t b_offset);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a74776be88be65092fe631a3313f46ab5">gemmlowp_matrix_multiply_core</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c, int32_t a_offset, int32_t b_offset);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#af18f21173e19d772347089a2839351b8">gemmlowp</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int8_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int8_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#af18f21173e19d772347089a2839351b8">gemmlowp</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;a, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_c);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160;} <span class="comment">// namespace reference</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160;} <span class="comment">// namespace validation</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5bab95cbeb5c6bf05049df7afd32d823"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">arm_compute::test::validation::asymm_rounding_divide_by_pow2</a></div><div class="ttdeci">int32_t asymm_rounding_divide_by_pow2(int32_t x, int exponent)</div><div class="ttdoc">Rounded to nearest division by a power-of-two.</div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00036">UtilsQuantizedAsymm.h:36</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::DataType::QSYMM16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number</div></div>
<div class="ttc" id="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="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00321">SimpleTensor.h:321</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a344296b0ad06f8e867f95658d59ecdeb"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a344296b0ad06f8e867f95658d59ecdeb">arm_compute::test::validation::reference::gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint</a></div><div class="ttdeci">SimpleTensor&lt; int16_t &gt; gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor&lt; T &gt; &amp;in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_g_e_m_m_lowp_8cpp_source.xhtml#l00233">GEMMLowp.cpp:233</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aea27abcd3d58d627282320dfdd213596"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">arm_compute::test::validation::asymm_int_mult</a></div><div class="ttdeci">int32_t asymm_int_mult(int32_t a, int32_t b)</div><div class="ttdoc">Multiplication of two integers.</div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00044">UtilsQuantizedAsymm.h:44</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a74776be88be65092fe631a3313f46ab5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a74776be88be65092fe631a3313f46ab5">arm_compute::test::validation::reference::gemmlowp_matrix_multiply_core</a></div><div class="ttdeci">SimpleTensor&lt; T_out &gt; gemmlowp_matrix_multiply_core(const SimpleTensor&lt; T_in &gt; &amp;a, const SimpleTensor&lt; T_in &gt; &amp;b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_g_e_m_m_lowp_8cpp_source.xhtml#l00129">GEMMLowp.cpp:129</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a06197dfafd5455257cec13e414cce105"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a06197dfafd5455257cec13e414cce105">arm_compute::test::validation::reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint</a></div><div class="ttdeci">SimpleTensor&lt; uint8_t &gt; gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor&lt; T &gt; &amp;in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_g_e_m_m_lowp_8cpp_source.xhtml#l00210">GEMMLowp.cpp:210</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00102">AbsoluteDifference.cpp:102</a></div></div>
<div class="ttc" id="_utils_quantized_asymm_8h_xhtml"><div class="ttname"><a href="_utils_quantized_asymm_8h.xhtml">UtilsQuantizedAsymm.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3a77be8aebd8e00522b32061d46ccdbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">arm_compute::test::validation::bias</a></div><div class="ttdeci">CLTensor bias</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00181">ConvolutionLayer.cpp:181</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</a></div></div>
<div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00074">Types.h:74</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_aa74f872dcd4daaaa3ef53e8ab628b606"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aa74f872dcd4daaaa3ef53e8ab628b606">arm_compute::test::validation::reference::gemmlowp_quantize_down_int32_to_uint8_scale</a></div><div class="ttdeci">SimpleTensor&lt; uint8_t &gt; gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor&lt; T &gt; &amp;in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_g_e_m_m_lowp_8cpp_source.xhtml#l00189">GEMMLowp.cpp:189</a></div></div>
<div class="ttc" id="_g_e_m_m_lowp_8h_xhtml"><div class="ttname"><a href="_g_e_m_m_lowp_8h.xhtml">GEMMLowp.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_af18f21173e19d772347089a2839351b8"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#af18f21173e19d772347089a2839351b8">arm_compute::test::validation::reference::gemmlowp</a></div><div class="ttdeci">SimpleTensor&lt; T1 &gt; gemmlowp(const SimpleTensor&lt; T2 &gt; &amp;a, const SimpleTensor&lt; T2 &gt; &amp;b, TensorShape shape_c)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_g_e_m_m_lowp_8cpp_source.xhtml#l00183">GEMMLowp.cpp:183</a></div></div>
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