Update Doxygen for 23.11
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I47cd933f5002cb94a73aa97689d7b3d9c93cb849
diff --git a/latest/_ref_unidirectional_sequence_lstm_workload_8cpp_source.html b/latest/_ref_unidirectional_sequence_lstm_workload_8cpp_source.html
index 4c90d1c..44ed637 100644
--- a/latest/_ref_unidirectional_sequence_lstm_workload_8cpp_source.html
+++ b/latest/_ref_unidirectional_sequence_lstm_workload_8cpp_source.html
@@ -36,7 +36,7 @@
<img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 15rem; margin-top: .5rem; margin-left 13px"/>
<td id="projectalign" style="padding-left: 0.9em;">
<div id="projectname">
-  <span id="projectnumber">23.08</span>
+  <span id="projectnumber">23.11</span>
</div>
</td>
</tr>
@@ -179,248 +179,249 @@
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  outputShape = <a class="code" href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), mappings);</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div>
-<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = inputShape[0];</div>
-<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = inputShape[1];</div>
-<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputShape[2];</div>
-<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputShape[2];</div>
-<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  </div>
-<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  TensorInfo scratchInfo = outputInfo;</div>
-<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  scratchInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({batchSize, cellStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1]});</div>
-<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  </div>
-<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::vector<float> inputGateScratchBuffer;</div>
-<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::vector<float> cellScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  std::vector<float> forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  std::vector<float> outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  </div>
-<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::vector<float> outputStateOutBuffer(outputStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0.);</div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::vector<float> cellStateOutBuffer(cellStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0.);</div>
-<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  </div>
-<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">void</span>* outputStateOutData = outputStateOutBuffer.data();</div>
-<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordtype">void</span>* cellStateOutData = cellStateOutBuffer.data();</div>
-<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  </div>
-<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::unique_ptr<Encoder<float>> inputGateScratch;</div>
-<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::unique_ptr<Encoder<float>> cellScratch = MakeEncoder<float>(scratchInfo, cellScratchBuffer.data());</div>
-<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::unique_ptr<Encoder<float>> forgetGateScratch = MakeEncoder<float>(scratchInfo, forgetGateScratchBuffer.data());</div>
-<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::unique_ptr<Encoder<float>> outputGateScratch = MakeEncoder<float>(scratchInfo, outputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  </div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  std::unique_ptr<Decoder<float>> inputGateScratchDecoder;</div>
-<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::unique_ptr<Decoder<float>> cellScratchDecoder = MakeDecoder<float>(scratchInfo, cellScratchBuffer.data());</div>
-<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::unique_ptr<Decoder<float>> forgetGateScratchDecoder = MakeDecoder<float>(scratchInfo,</div>
-<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  forgetGateScratchBuffer.data());</div>
-<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::unique_ptr<Decoder<float>> outputGateScratchDecoder = MakeDecoder<float>(scratchInfo,</div>
-<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  outputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  </div>
-<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useCifg = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div>
-<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> usePeephole = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div>
-<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useLayerNorm = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</div>
-<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  </div>
-<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">if</span> (!useCifg)</div>
-<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  {</div>
-<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  inputGateScratch = MakeEncoder<float>(scratchInfo, inputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  inputGateScratchDecoder = MakeDecoder<float>(scratchInfo, inputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div>
-<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  </div>
-<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputStateInfo, outputStateOutData);</div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::unique_ptr<Encoder<float>> cellStateOut = MakeEncoder<float>(cellStateInfo, cellStateOutData);</div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::unique_ptr<Decoder<float>> cellStateOutDecoder = MakeDecoder<float>(cellStateInfo, cellStateOutData);</div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  </div>
-<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  TensorInfo lstmInputInfo = inputInfo;</div>
-<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  TensorShape batchInputShape = TensorShape({batchSize, inputSize});</div>
-<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  lstmInputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(batchInputShape);</div>
-<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  </div>
-<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  TensorInfo lstmOutputInfo = outputInfo;</div>
-<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  lstmOutputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({batchSize, outputSize});</div>
-<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  </div>
-<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">const</span> TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();</div>
-<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();</div>
-<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nOutput = recurrentToOutputWeightsShape[1];</div>
-<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">auto</span> outputStateInData = inputs[1]->Map();</div>
-<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  std::unique_ptr<Decoder<float>> outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateInData);</div>
-<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  </div>
-<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">auto</span> cellStateInData = inputs[2]->Map();</div>
-<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateInData);</div>
-<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  </div>
-<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">auto</span> currentInputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(inputs[0]->Map());</div>
-<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);</div>
-<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">auto</span> currentOutputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(outputs[2]->Map());</div>
-<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  </div>
-<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  std::unique_ptr<Decoder<float>> inputToInputWeightsTensor;</div>
-<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::unique_ptr<Decoder<float>> inputToForgetWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::unique_ptr<Decoder<float>> inputToCellWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  std::unique_ptr<Decoder<float>> inputToOutputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  </div>
-<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  std::unique_ptr<Decoder<float>> recurrentToInputWeightsTensor;</div>
-<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  std::unique_ptr<Decoder<float>> recurrentToForgetWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::unique_ptr<Decoder<float>> recurrentToCellWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  std::unique_ptr<Decoder<float>> recurrentToOutputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  </div>
-<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  std::unique_ptr<Decoder<float>> inputGateBiasTensor;</div>
-<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::unique_ptr<Decoder<float>> forgetGateBiasTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  m_ForgetGateBiasTensor->GetTensorInfo(), m_ForgetGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::unique_ptr<Decoder<float>> cellBiasTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  m_CellBiasTensor->GetTensorInfo(), m_CellBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  std::unique_ptr<Decoder<float>> outputGateBiasTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  m_OutputGateBiasTensor->GetTensorInfo(), m_OutputGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  </div>
-<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::unique_ptr<Decoder<float>> cellToInputWeightsTensor;</div>
-<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::unique_ptr<Decoder<float>> cellToForgetWeightsTensor;</div>
-<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  std::unique_ptr<Decoder<float>> cellToOutputWeightsTensor;</div>
-<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  </div>
-<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  std::unique_ptr<Decoder<float>> projectionWeightsTensor;</div>
-<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  std::unique_ptr<Decoder<float>> projectionBiasTensor;</div>
-<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  </div>
-<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  std::unique_ptr<Decoder<float>> inputLayerNormWeights;</div>
-<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::unique_ptr<Decoder<float>> forgetLayerNormWeights;</div>
-<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  std::unique_ptr<Decoder<float>> cellLayerNormWeights;</div>
-<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  std::unique_ptr<Decoder<float>> outputLayerNormWeights;</div>
-<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  </div>
-<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span> (useLayerNorm)</div>
-<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div>
-<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">if</span> (!useCifg)</div>
-<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  {</div>
-<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  inputLayerNormWeights = MakeDecoder<float>(</div>
-<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  m_InputLayerNormWeights->GetTensorInfo(), m_InputLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div>
-<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  forgetLayerNormWeights = MakeDecoder<float>(</div>
-<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  m_ForgetLayerNormWeights->GetTensorInfo(), m_ForgetLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  cellLayerNormWeights = MakeDecoder<float>(</div>
-<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  m_CellLayerNormWeights->GetTensorInfo(), m_CellLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  outputLayerNormWeights = MakeDecoder<float>(</div>
-<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  m_OutputLayerNormWeights->GetTensorInfo(), m_OutputLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  }</div>
-<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  </div>
-<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">if</span> (!useCifg)</div>
-<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div>
-<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  inputToInputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  inputGateBiasTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  m_InputGateBiasTensor->GetTensorInfo(), m_InputGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  recurrentToInputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  }</div>
-<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  </div>
-<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">if</span> (usePeephole)</div>
-<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div>
-<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  cellToForgetWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  cellToOutputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  }</div>
-<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  </div>
-<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">if</span> (!useCifg && usePeephole)</div>
-<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div>
-<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  cellToInputWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div>
-<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  </div>
-<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div>
-<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div>
-<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  projectionWeightsTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
-<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div>
-<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  projectionBiasTensor = MakeDecoder<float>(</div>
-<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
-<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div>
-<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</div>
-<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  </div>
-<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchInputSize = batchSize * inputSize;</div>
-<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchOutputSize = batchSize * nOutput;</div>
-<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  </div>
-<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> t = 0; t < maxTime; ++t)</div>
-<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div>
-<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="namespacearmnn.html#a952423703fa6b92f18d19df3995633b4">LstmImpl</a>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div>
-<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  lstmInputInfo,</div>
-<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  lstmOutputInfo,</div>
-<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  inputToOutputWeightsShape,</div>
-<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  recurrentToOutputWeightsShape,</div>
-<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  inputData,</div>
-<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  outputStateIn,</div>
-<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  cellStateIn,</div>
-<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  outputStateOut,</div>
-<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  cellStateOut,</div>
-<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  output,</div>
-<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  cellStateOutDecoder,</div>
-<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  outputDecoder,</div>
-<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  inputToInputWeightsTensor,</div>
-<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  inputToForgetWeightsTensor,</div>
-<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  inputToCellWeightsTensor,</div>
-<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  inputToOutputWeightsTensor,</div>
-<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  recurrentToInputWeightsTensor,</div>
-<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  recurrentToForgetWeightsTensor,</div>
-<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  recurrentToCellWeightsTensor,</div>
-<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  recurrentToOutputWeightsTensor,</div>
-<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  cellToInputWeightsTensor,</div>
-<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  cellToForgetWeightsTensor,</div>
-<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  cellToOutputWeightsTensor,</div>
-<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  inputGateBiasTensor,</div>
-<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  forgetGateBiasTensor,</div>
-<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  cellBiasTensor,</div>
-<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  outputGateBiasTensor,</div>
-<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  projectionWeightsTensor,</div>
-<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  projectionBiasTensor,</div>
-<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  inputLayerNormWeights,</div>
-<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  forgetLayerNormWeights,</div>
-<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  cellLayerNormWeights,</div>
-<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  outputLayerNormWeights,</div>
-<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  inputGateScratch,</div>
-<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  cellScratch,</div>
-<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  forgetGateScratch,</div>
-<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  outputGateScratch,</div>
-<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  inputGateScratchDecoder,</div>
-<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  cellScratchDecoder,</div>
-<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  forgetGateScratchDecoder,</div>
-<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  outputGateScratchDecoder,</div>
-<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  m_LayerNormEpsilon);</div>
-<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  </div>
-<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  currentInputData += batchInputSize;</div>
-<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);</div>
-<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  currentOutputData += batchOutputSize;</div>
-<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  </div>
-<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// Assign output state out to the next output state in</span></div>
-<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateOutData);</div>
-<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  </div>
-<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="comment">// Assign cell state out to the next cell state in</span></div>
-<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateOutData);</div>
-<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div>
-<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  </div>
-<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
-<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div>
-<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// Permute Output back to batch major</span></div>
-<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> PermutationVector& mappings = {1U, 0U, 2U};</div>
-<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">auto</span> outputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(outputs[2]->Map());</div>
-<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  std::vector<float> outputValue(outputData, outputData + outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
-<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  outputShape = <a class="code" href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), mappings);</div>
-<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div>
-<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputShape, mappings, outputValue.data(), outputData, <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div>
-<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div>
-<div class="line"><a name="l00310"></a><span class="lineno"> 310</span> }</div>
-<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  </div>
-<div class="line"><a name="l00312"></a><span class="lineno"> 312</span> } <span class="comment">//namespace armnn</span></div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// As it is permuted to time major, maxTime is inputShape[0].</span></div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = inputShape[0];</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = inputShape[1];</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputShape[2];</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputShape[2];</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  </div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  TensorInfo scratchInfo = outputInfo;</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  scratchInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({batchSize, cellStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1]});</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  </div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::vector<float> inputGateScratchBuffer;</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  std::vector<float> cellScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  std::vector<float> forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  std::vector<float> outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  </div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::vector<float> outputStateOutBuffer(outputStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0.);</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  std::vector<float> cellStateOutBuffer(cellStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0.);</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  </div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordtype">void</span>* outputStateOutData = outputStateOutBuffer.data();</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordtype">void</span>* cellStateOutData = cellStateOutBuffer.data();</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  </div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::unique_ptr<Encoder<float>> inputGateScratch;</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::unique_ptr<Encoder<float>> cellScratch = MakeEncoder<float>(scratchInfo, cellScratchBuffer.data());</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::unique_ptr<Encoder<float>> forgetGateScratch = MakeEncoder<float>(scratchInfo, forgetGateScratchBuffer.data());</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::unique_ptr<Encoder<float>> outputGateScratch = MakeEncoder<float>(scratchInfo, outputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  </div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::unique_ptr<Decoder<float>> inputGateScratchDecoder;</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::unique_ptr<Decoder<float>> cellScratchDecoder = MakeDecoder<float>(scratchInfo, cellScratchBuffer.data());</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::unique_ptr<Decoder<float>> forgetGateScratchDecoder = MakeDecoder<float>(scratchInfo,</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  forgetGateScratchBuffer.data());</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::unique_ptr<Decoder<float>> outputGateScratchDecoder = MakeDecoder<float>(scratchInfo,</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  outputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  </div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useCifg = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> usePeephole = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useLayerNorm = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  </div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">if</span> (!useCifg)</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  inputGateScratch = MakeEncoder<float>(scratchInfo, inputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  inputGateScratchDecoder = MakeDecoder<float>(scratchInfo, inputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  </div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputStateInfo, outputStateOutData);</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::unique_ptr<Encoder<float>> cellStateOut = MakeEncoder<float>(cellStateInfo, cellStateOutData);</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::unique_ptr<Decoder<float>> cellStateOutDecoder = MakeDecoder<float>(cellStateInfo, cellStateOutData);</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  </div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  TensorInfo lstmInputInfo = inputInfo;</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  TensorShape batchInputShape = TensorShape({batchSize, inputSize});</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  lstmInputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(batchInputShape);</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  </div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  TensorInfo lstmOutputInfo = outputInfo;</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  lstmOutputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({batchSize, outputSize});</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  </div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nOutput = recurrentToOutputWeightsShape[1];</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">auto</span> outputStateInData = inputs[1]->Map();</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::unique_ptr<Decoder<float>> outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateInData);</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  </div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">auto</span> cellStateInData = inputs[2]->Map();</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateInData);</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  </div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">auto</span> currentInputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(inputs[0]->Map());</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">auto</span> currentOutputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(outputs[2]->Map());</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  </div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::unique_ptr<Decoder<float>> inputToInputWeightsTensor;</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::unique_ptr<Decoder<float>> inputToForgetWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  std::unique_ptr<Decoder<float>> inputToCellWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  std::unique_ptr<Decoder<float>> inputToOutputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  </div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  std::unique_ptr<Decoder<float>> recurrentToInputWeightsTensor;</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  std::unique_ptr<Decoder<float>> recurrentToForgetWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  std::unique_ptr<Decoder<float>> recurrentToCellWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  std::unique_ptr<Decoder<float>> recurrentToOutputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  </div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::unique_ptr<Decoder<float>> inputGateBiasTensor;</div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  std::unique_ptr<Decoder<float>> forgetGateBiasTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  m_ForgetGateBiasTensor->GetTensorInfo(), m_ForgetGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  std::unique_ptr<Decoder<float>> cellBiasTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  m_CellBiasTensor->GetTensorInfo(), m_CellBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  std::unique_ptr<Decoder<float>> outputGateBiasTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  m_OutputGateBiasTensor->GetTensorInfo(), m_OutputGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  </div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::unique_ptr<Decoder<float>> cellToInputWeightsTensor;</div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  std::unique_ptr<Decoder<float>> cellToForgetWeightsTensor;</div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  std::unique_ptr<Decoder<float>> cellToOutputWeightsTensor;</div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  </div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  std::unique_ptr<Decoder<float>> projectionWeightsTensor;</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  std::unique_ptr<Decoder<float>> projectionBiasTensor;</div>
+<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  </div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::unique_ptr<Decoder<float>> inputLayerNormWeights;</div>
+<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  std::unique_ptr<Decoder<float>> forgetLayerNormWeights;</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  std::unique_ptr<Decoder<float>> cellLayerNormWeights;</div>
+<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  std::unique_ptr<Decoder<float>> outputLayerNormWeights;</div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  </div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">if</span> (useLayerNorm)</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">if</span> (!useCifg)</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  inputLayerNormWeights = MakeDecoder<float>(</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  m_InputLayerNormWeights->GetTensorInfo(), m_InputLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  forgetLayerNormWeights = MakeDecoder<float>(</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  m_ForgetLayerNormWeights->GetTensorInfo(), m_ForgetLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  cellLayerNormWeights = MakeDecoder<float>(</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  m_CellLayerNormWeights->GetTensorInfo(), m_CellLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  outputLayerNormWeights = MakeDecoder<float>(</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  m_OutputLayerNormWeights->GetTensorInfo(), m_OutputLayerNormWeights->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  }</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  </div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">if</span> (!useCifg)</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  inputToInputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  inputGateBiasTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  m_InputGateBiasTensor->GetTensorInfo(), m_InputGateBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  recurrentToInputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  </div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">if</span> (usePeephole)</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  cellToForgetWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  cellToOutputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div>
+<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  </div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">if</span> (!useCifg && usePeephole)</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  {</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  cellToInputWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  </div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div>
+<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  projectionWeightsTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  projectionBiasTensor = MakeDecoder<float>(</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetConstTensor<<span class="keywordtype">void</span>>());</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  }</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  </div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchInputSize = batchSize * inputSize;</div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchOutputSize = batchSize * nOutput;</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  </div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> t = 0; t < maxTime; ++t)</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="namespacearmnn.html#a952423703fa6b92f18d19df3995633b4">LstmImpl</a>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  lstmInputInfo,</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  lstmOutputInfo,</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  inputToOutputWeightsShape,</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  recurrentToOutputWeightsShape,</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  inputData,</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  outputStateIn,</div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  cellStateIn,</div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  outputStateOut,</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  cellStateOut,</div>
+<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  output,</div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  cellStateOutDecoder,</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  outputDecoder,</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  inputToInputWeightsTensor,</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  inputToForgetWeightsTensor,</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  inputToCellWeightsTensor,</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  inputToOutputWeightsTensor,</div>
+<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  recurrentToInputWeightsTensor,</div>
+<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  recurrentToForgetWeightsTensor,</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  recurrentToCellWeightsTensor,</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  recurrentToOutputWeightsTensor,</div>
+<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  cellToInputWeightsTensor,</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  cellToForgetWeightsTensor,</div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  cellToOutputWeightsTensor,</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  inputGateBiasTensor,</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  forgetGateBiasTensor,</div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  cellBiasTensor,</div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  outputGateBiasTensor,</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  projectionWeightsTensor,</div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  projectionBiasTensor,</div>
+<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  inputLayerNormWeights,</div>
+<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  forgetLayerNormWeights,</div>
+<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  cellLayerNormWeights,</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  outputLayerNormWeights,</div>
+<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  inputGateScratch,</div>
+<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  cellScratch,</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  forgetGateScratch,</div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  outputGateScratch,</div>
+<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  inputGateScratchDecoder,</div>
+<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  cellScratchDecoder,</div>
+<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  forgetGateScratchDecoder,</div>
+<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  outputGateScratchDecoder,</div>
+<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  m_LayerNormEpsilon);</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  </div>
+<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  currentInputData += batchInputSize;</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);</div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  currentOutputData += batchOutputSize;</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  </div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="comment">// Assign output state out to the next output state in</span></div>
+<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateOutData);</div>
+<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  </div>
+<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// Assign cell state out to the next cell state in</span></div>
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateOutData);</div>
+<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  }</div>
+<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  </div>
+<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.html#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  {</div>
+<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// Permute Output back to batch major</span></div>
+<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> PermutationVector& mappings = {1U, 0U, 2U};</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">auto</span> outputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(outputs[2]->Map());</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::vector<float> outputValue(outputData, outputData + outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
+<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  outputShape = <a class="code" href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), mappings);</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputShape, mappings, outputValue.data(), outputData, <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span> }</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  </div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> } <span class="comment">//namespace armnn</span></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00196">Tensor.hpp:196</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a3dcd10ca3ea2e132558b1e2814668c15"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a3dcd10ca3ea2e132558b1e2814668c15">armnn::LstmDescriptor::m_TimeMajor</a></div><div class="ttdeci">bool m_TimeMajor</div><div class="ttdoc">Enable/disable time major.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01133">Descriptors.hpp:1133</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a3dcd10ca3ea2e132558b1e2814668c15"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a3dcd10ca3ea2e132558b1e2814668c15">armnn::LstmDescriptor::m_TimeMajor</a></div><div class="ttdeci">bool m_TimeMajor</div><div class="ttdoc">Enable/disable time major.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01154">Descriptors.hpp:1154</a></div></div>
<div class="ttc" id="a_lstm_8hpp_html"><div class="ttname"><a href="_lstm_8hpp.html">Lstm.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1experimental_1_1_execution_data_html_ad2b382076f26f48cd44783cfca2e3642"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_execution_data.html#ad2b382076f26f48cd44783cfca2e3642">armnn::experimental::ExecutionData::m_Data</a></div><div class="ttdeci">void * m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_execution_data_8hpp_source.html#l00016">ExecutionData.hpp:16</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="a_lstm_utils_8cpp_html_a8618ae0c77638e01069fdb0063cabb3f"><div class="ttname"><a href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a></div><div class="ttdeci">std::unique_ptr< armnn::ScopedTensorHandle > AssignScopedTensorHandle(const armnn::ConstTensorHandle *ptr)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00299">LstmUtils.cpp:299</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_ref_unidirectional_sequence_lstm_workload_html_ae1c43d025fc90382d7aff7a500937e2c"><div class="ttname"><a href="classarmnn_1_1_ref_unidirectional_sequence_lstm_workload.html#ae1c43d025fc90382d7aff7a500937e2c">armnn::RefUnidirectionalSequenceLstmWorkload::ExecuteAsync</a></div><div class="ttdeci">void ExecuteAsync(ExecutionData &executionData) override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_unidirectional_sequence_lstm_workload_8cpp_source.html#l00051">RefUnidirectionalSequenceLstmWorkload.cpp:51</a></div></div>
<div class="ttc" id="a_ref_workload_utils_8hpp_html_a06acca4fd832e0fb179604112c0505af"><div class="ttname"><a href="_ref_workload_utils_8hpp.html#a06acca4fd832e0fb179604112c0505af">ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID(label)</div><div class="ttdoc">Creates a profiling event that uses GetGuid() and GetName() from the calling class.</div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.html#l00022">RefWorkloadUtils.hpp:22</a></div></div>
-<div class="ttc" id="anamespacearmnn_utils_html_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00131">Permute.cpp:131</a></div></div>
-<div class="ttc" id="anamespacearmnn_utils_html_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00098">Permute.cpp:98</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01127">Descriptors.hpp:1127</a></div></div>
+<div class="ttc" id="anamespacearmnn_utils_html_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00164">Permute.cpp:164</a></div></div>
+<div class="ttc" id="anamespacearmnn_utils_html_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00125">Permute.cpp:125</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01148">Descriptors.hpp:1148</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a952423703fa6b92f18d19df3995633b4"><div class="ttname"><a href="namespacearmnn.html#a952423703fa6b92f18d19df3995633b4">armnn::LstmImpl</a></div><div class="ttdeci">void LstmImpl(const LstmDescriptor &descriptor, const TensorInfo &inputInfo, const TensorInfo &outputInfo, const TensorShape &inputToOutputWeightsShape, const TensorShape &recurrentToOutputWeightsShape, std::unique_ptr< Decoder< float >> &inputData, std::unique_ptr< Decoder< float >> &outputStateIn, std::unique_ptr< Decoder< float >> &cellStateIn, std::unique_ptr< Encoder< float >> &outputStateOut, std::unique_ptr< Encoder< float >> &cellStateOut, std::unique_ptr< Encoder< float >> &output, std::unique_ptr< Decoder< float >> &cellStateOutDecoder, std::unique_ptr< Decoder< float >> &outputDecoder, std::unique_ptr< Decoder< float >> &inputToInputWeightsTensor, std::unique_ptr< Decoder< float >> &inputToForgetWeightsTensor, std::unique_ptr< Decoder< float >> &inputToCellWeightsTensor, std::unique_ptr< Decoder< float >> &inputToOutputWeightsTensor, std::unique_ptr< Decoder< float >> &recurrentToInputWeightsTensor, std::unique_ptr< Decoder< float >> &recurrentToForgetWeightsTensor, std::unique_ptr< Decoder< float >> &recurrentToCellWeightsTensor, std::unique_ptr< Decoder< float >> &recurrentToOutputWeightsTensor, std::unique_ptr< Decoder< float >> &cellToInputWeightsTensor, std::unique_ptr< Decoder< float >> &cellToForgetWeightsTensor, std::unique_ptr< Decoder< float >> &cellToOutputWeightsTensor, std::unique_ptr< Decoder< float >> &inputGateBiasTensor, std::unique_ptr< Decoder< float >> &forgetGateBiasTensor, std::unique_ptr< Decoder< float >> &cellBiasTensor, std::unique_ptr< Decoder< float >> &outputGateBiasTensor, std::unique_ptr< Decoder< float >> &projectionWeightsTensor, std::unique_ptr< Decoder< float >> &projectionBiasTensor, std::unique_ptr< Decoder< float >> &inputLayerNormWeights, std::unique_ptr< Decoder< float >> &forgetLayerNormWeights, std::unique_ptr< Decoder< float >> &cellLayerNormWeights, std::unique_ptr< Decoder< float >> &outputLayerNormWeights, std::unique_ptr< Encoder< float >> &inputGateScratch, std::unique_ptr< Encoder< float >> &cellScratch, std::unique_ptr< Encoder< float >> &forgetGateScratch, std::unique_ptr< Encoder< float >> &outputGateScratch, std::unique_ptr< Decoder< float >> &inputGateScratchDecoder, std::unique_ptr< Decoder< float >> &cellScratchDecoder, std::unique_ptr< Decoder< float >> &forgetGateScratchDecoder, std::unique_ptr< Decoder< float >> &outputGateScratchDecoder, float layerNormEpsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_8cpp_source.html#l00013">Lstm.cpp:13</a></div></div>
<div class="ttc" id="astructarmnn_1_1_queue_descriptor_with_parameters_html_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00066">WorkloadData.hpp:66</a></div></div>
@@ -428,23 +429,23 @@
<div class="ttc" id="aclassarmnn_1_1_ref_unidirectional_sequence_lstm_workload_html_a012324a56cbf03d956d317178a0c4923"><div class="ttname"><a href="classarmnn_1_1_ref_unidirectional_sequence_lstm_workload.html#a012324a56cbf03d956d317178a0c4923">armnn::RefUnidirectionalSequenceLstmWorkload::RefUnidirectionalSequenceLstmWorkload</a></div><div class="ttdeci">RefUnidirectionalSequenceLstmWorkload(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_ref_unidirectional_sequence_lstm_workload_8cpp_source.html#l00019">RefUnidirectionalSequenceLstmWorkload.cpp:19</a></div></div>
<div class="ttc" id="anamespacearmnn_html_aa815fde54f6d8e8aa5b4f0301cf4178b"><div class="ttname"><a href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers</div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.html#l00033">RefWorkloadUtils.hpp:33</a></div></div>
<div class="ttc" id="a_activation_8hpp_html"><div class="ttname"><a href="_activation_8hpp.html">Activation.hpp</a></div></div>
-<div class="ttc" id="aclassarmnn_1_1_permutation_vector_html"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00308">Types.hpp:308</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_permutation_vector_html"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00314">Types.hpp:314</a></div></div>
<div class="ttc" id="a_permute_8hpp_html"><div class="ttname"><a href="_permute_8hpp.html">Permute.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div>
<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00027">WorkloadData.hpp:27</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_ref_unidirectional_sequence_lstm_workload_html_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_ref_unidirectional_sequence_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">armnn::RefUnidirectionalSequenceLstmWorkload::Execute</a></div><div class="ttdeci">void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_unidirectional_sequence_lstm_workload_8cpp_source.html#l00046">RefUnidirectionalSequenceLstmWorkload.cpp:46</a></div></div>
<div class="ttc" id="a_ref_workload_utils_8hpp_html"><div class="ttname"><a href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01125">Descriptors.hpp:1125</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01146">Descriptors.hpp:1146</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_base_workload_html_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload< UnidirectionalSequenceLstmQueueDescriptor >::m_Data</a></div><div class="ttdeci">UnidirectionalSequenceLstmQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00089">Workload.hpp:89</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00191">Tensor.hpp:191</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01131">Descriptors.hpp:1131</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01152">Descriptors.hpp:1152</a></div></div>
<div class="ttc" id="a_decoders_8hpp_html"><div class="ttname"><a href="_decoders_8hpp.html">Decoders.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1experimental_1_1_working_mem_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::experimental::WorkingMemDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.html#l00020">WorkingMemDescriptor.hpp:20</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="astructarmnn_1_1experimental_1_1_working_mem_descriptor_html"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">armnn::experimental::WorkingMemDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.html#l00018">WorkingMemDescriptor.hpp:18</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01129">Descriptors.hpp:1129</a></div></div>
-<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00691">WorkloadData.hpp:691</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_html_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01150">Descriptors.hpp:1150</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00696">WorkloadData.hpp:696</a></div></div>
<div class="ttc" id="a_lstm_utils_8hpp_html"><div class="ttname"><a href="_lstm_utils_8hpp.html">LstmUtils.hpp</a></div></div>
<div class="ttc" id="a_encoders_8hpp_html"><div class="ttname"><a href="_encoders_8hpp.html">Encoders.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_ref_base_workload_html"><div class="ttname"><a href="classarmnn_1_1_ref_base_workload.html">armnn::RefBaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_base_workload_8hpp_source.html#l00013">RefBaseWorkload.hpp:13</a></div></div>
@@ -456,7 +457,7 @@
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<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.html">backends</a></li><li class="navelem"><a class="el" href="dir_efae4012d0e357ebeaba7d02491d70e5.html">reference</a></li><li class="navelem"><a class="el" href="dir_d2f3b8e2e64df3181ebe92efcc0a3012.html">workloads</a></li><li class="navelem"><a class="el" href="_ref_unidirectional_sequence_lstm_workload_8cpp.html">RefUnidirectionalSequenceLstmWorkload.cpp</a></li>
- <li class="footer">Generated on Tue Aug 22 2023 11:37:00 for Arm NN by
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