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">
-   &#160;<span id="projectnumber">23.08</span>
+   &#160;<span id="projectnumber">23.11</span>
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   </td>
  </tr>
@@ -179,248 +179,249 @@
 <div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        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>&#160;        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>&#160;    }</div>
-<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <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>&#160;    <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>&#160;    <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>&#160;    <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>&#160; </div>
-<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    TensorInfo scratchInfo = outputInfo;</div>
-<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    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>&#160; </div>
-<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    std::vector&lt;float&gt; inputGateScratchBuffer;</div>
-<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    std::vector&lt;float&gt; cellScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    std::vector&lt;float&gt; forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    std::vector&lt;float&gt; outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</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;    std::vector&lt;float&gt; 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>&#160;    std::vector&lt;float&gt; 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>&#160; </div>
-<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <span class="keywordtype">void</span>* outputStateOutData = outputStateOutBuffer.data();</div>
-<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keywordtype">void</span>* cellStateOutData = cellStateOutBuffer.data();</div>
-<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160; </div>
-<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; inputGateScratch;</div>
-<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellScratch = MakeEncoder&lt;float&gt;(scratchInfo, cellScratchBuffer.data());</div>
-<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; forgetGateScratch = MakeEncoder&lt;float&gt;(scratchInfo, forgetGateScratchBuffer.data());</div>
-<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputGateScratch = MakeEncoder&lt;float&gt;(scratchInfo, outputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
-<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateScratchDecoder;</div>
-<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo, cellScratchBuffer.data());</div>
-<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo,</div>
-<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                                                                  forgetGateScratchBuffer.data());</div>
-<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo,</div>
-<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                                                                                  outputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160; </div>
-<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <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>&#160;    <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>&#160;    <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>&#160; </div>
-<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keywordflow">if</span> (!useCifg)</div>
-<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    {</div>
-<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);</div>
-<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        inputGateScratch = MakeEncoder&lt;float&gt;(scratchInfo, inputGateScratchBuffer.data());</div>
-<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        inputGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo, inputGateScratchBuffer.data());</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;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputStateOut = MakeEncoder&lt;float&gt;(outputStateInfo, outputStateOutData);</div>
-<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellStateOut   = MakeEncoder&lt;float&gt;(cellStateInfo, cellStateOutData);</div>
-<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateOutDecoder = MakeDecoder&lt;float&gt;(cellStateInfo, cellStateOutData);</div>
-<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
-<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    TensorInfo lstmInputInfo = inputInfo;</div>
-<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    TensorShape batchInputShape = TensorShape({batchSize, inputSize});</div>
-<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    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>&#160; </div>
-<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    TensorInfo lstmOutputInfo = outputInfo;</div>
-<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    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>&#160; </div>
-<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="keyword">const</span> TensorShape&amp; inputToOutputWeightsShape = m_InputToOutputWeightsTensor-&gt;GetShape();</div>
-<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keyword">const</span> TensorShape&amp; recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor-&gt;GetShape();</div>
-<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <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>&#160;    <span class="keyword">auto</span> outputStateInData = inputs[1]-&gt;Map();</div>
-<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateIn = MakeDecoder&lt;float&gt;(outputStateInfo, outputStateInData);</div>
-<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160; </div>
-<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keyword">auto</span> cellStateInData = inputs[2]-&gt;Map();</div>
-<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateIn = MakeDecoder&lt;float&gt;(cellStateInfo, cellStateInData);</div>
-<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160; </div>
-<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <span class="keyword">auto</span> currentInputData = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(inputs[0]-&gt;Map());</div>
-<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputData = MakeDecoder&lt;float&gt;(lstmInputInfo, currentInputData);</div>
-<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="keyword">auto</span> currentOutputData = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(outputs[2]-&gt;Map());</div>
-<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; output = MakeEncoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputDecoder = MakeDecoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
-<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; </div>
-<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToInputWeightsTensor;</div>
-<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToForgetWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        m_InputToForgetWeightsTensor-&gt;GetTensorInfo(), m_InputToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToCellWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        m_InputToCellWeightsTensor-&gt;GetTensorInfo(), m_InputToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToOutputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        m_InputToOutputWeightsTensor-&gt;GetTensorInfo(), m_InputToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160; </div>
-<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToInputWeightsTensor;</div>
-<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToForgetWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        m_RecurrentToForgetWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToCellWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        m_RecurrentToCellWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToOutputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;        m_RecurrentToOutputWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160; </div>
-<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateBiasTensor;</div>
-<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateBiasTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        m_ForgetGateBiasTensor-&gt;GetTensorInfo(), m_ForgetGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellBiasTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        m_CellBiasTensor-&gt;GetTensorInfo(), m_CellBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateBiasTensor = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;        m_OutputGateBiasTensor-&gt;GetTensorInfo(), m_OutputGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToInputWeightsTensor;</div>
-<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToForgetWeightsTensor;</div>
-<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToOutputWeightsTensor;</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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionWeightsTensor;</div>
-<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionBiasTensor;</div>
-<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; </div>
-<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputLayerNormWeights;</div>
-<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetLayerNormWeights;</div>
-<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellLayerNormWeights;</div>
-<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputLayerNormWeights;</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="keywordflow">if</span> (useLayerNorm)</div>
-<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    {</div>
-<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        <span class="keywordflow">if</span> (!useCifg)</div>
-<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        {</div>
-<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;            inputLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;                    m_InputLayerNormWeights-&gt;GetTensorInfo(), m_InputLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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;        forgetLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
-<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;                m_ForgetLayerNormWeights-&gt;GetTensorInfo(), m_ForgetLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
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-<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;                m_CellLayerNormWeights-&gt;GetTensorInfo(), m_CellLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
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-<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    {</div>
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-<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            m_ProjectionWeightsTensor-&gt;GetTensorInfo(), m_ProjectionWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
-<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
-<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        {</div>
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-<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;                m_ProjectionBiasTensor-&gt;GetTensorInfo(), m_ProjectionBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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;    }</div>
-<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
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-<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    <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>&#160; </div>
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-<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    {</div>
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-<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;                 lstmInputInfo,</div>
-<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;                 lstmOutputInfo,</div>
-<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;                 inputToOutputWeightsShape,</div>
-<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                 recurrentToOutputWeightsShape,</div>
-<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;                 inputData,</div>
-<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;                 outputStateIn,</div>
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-<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;                 outputStateOut,</div>
-<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;                 cellStateOut,</div>
-<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                 output,</div>
-<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                 cellStateOutDecoder,</div>
-<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                 outputDecoder,</div>
-<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;                 inputToInputWeightsTensor,</div>
-<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;                 inputToForgetWeightsTensor,</div>
-<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;                 inputToCellWeightsTensor,</div>
-<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;                 inputToOutputWeightsTensor,</div>
-<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;                 recurrentToInputWeightsTensor,</div>
-<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                 recurrentToForgetWeightsTensor,</div>
-<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;                 recurrentToCellWeightsTensor,</div>
-<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;                 recurrentToOutputWeightsTensor,</div>
-<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;                 cellToInputWeightsTensor,</div>
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-<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;                 cellToOutputWeightsTensor,</div>
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-<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;                 forgetGateBiasTensor,</div>
-<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;                 cellBiasTensor,</div>
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-<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;                 cellLayerNormWeights,</div>
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-<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;                 outputGateScratch,</div>
-<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                 inputGateScratchDecoder,</div>
-<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                 cellScratchDecoder,</div>
-<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                 forgetGateScratchDecoder,</div>
-<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                 outputGateScratchDecoder,</div>
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-<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160; </div>
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-<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        <span class="comment">// Permute Output back to batch major</span></div>
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-<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;}</div>
-<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
-<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;} <span class="comment">//namespace armnn</span></div>
+<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <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>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = inputShape[0];</div>
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+<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
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+<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160; </div>
+<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    std::vector&lt;float&gt; inputGateScratchBuffer;</div>
+<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    std::vector&lt;float&gt; cellScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    std::vector&lt;float&gt; forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    std::vector&lt;float&gt; outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);</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;    std::vector&lt;float&gt; 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>&#160;    std::vector&lt;float&gt; cellStateOutBuffer(cellStateInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0.);</div>
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+<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keywordtype">void</span>* cellStateOutData = cellStateOutBuffer.data();</div>
+<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
+<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; inputGateScratch;</div>
+<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellScratch = MakeEncoder&lt;float&gt;(scratchInfo, cellScratchBuffer.data());</div>
+<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; forgetGateScratch = MakeEncoder&lt;float&gt;(scratchInfo, forgetGateScratchBuffer.data());</div>
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+<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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateScratchDecoder;</div>
+<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo, cellScratchBuffer.data());</div>
+<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo,</div>
+<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                                                                                  forgetGateScratchBuffer.data());</div>
+<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo,</div>
+<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                                                                                  outputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160; </div>
+<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <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>&#160;    <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>&#160;    <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>&#160; </div>
+<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="keywordflow">if</span> (!useCifg)</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;        inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);</div>
+<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        inputGateScratch = MakeEncoder&lt;float&gt;(scratchInfo, inputGateScratchBuffer.data());</div>
+<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        inputGateScratchDecoder = MakeDecoder&lt;float&gt;(scratchInfo, inputGateScratchBuffer.data());</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; </div>
+<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputStateOut = MakeEncoder&lt;float&gt;(outputStateInfo, outputStateOutData);</div>
+<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellStateOut   = MakeEncoder&lt;float&gt;(cellStateInfo, cellStateOutData);</div>
+<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateOutDecoder = MakeDecoder&lt;float&gt;(cellStateInfo, cellStateOutData);</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;    TensorInfo lstmInputInfo = inputInfo;</div>
+<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    TensorShape batchInputShape = TensorShape({batchSize, inputSize});</div>
+<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    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>&#160; </div>
+<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    TensorInfo lstmOutputInfo = outputInfo;</div>
+<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    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>&#160; </div>
+<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keyword">const</span> TensorShape&amp; inputToOutputWeightsShape = m_InputToOutputWeightsTensor-&gt;GetShape();</div>
+<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="keyword">const</span> TensorShape&amp; recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor-&gt;GetShape();</div>
+<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <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>&#160;    <span class="keyword">auto</span> outputStateInData = inputs[1]-&gt;Map();</div>
+<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateIn = MakeDecoder&lt;float&gt;(outputStateInfo, outputStateInData);</div>
+<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160; </div>
+<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="keyword">auto</span> cellStateInData = inputs[2]-&gt;Map();</div>
+<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateIn = MakeDecoder&lt;float&gt;(cellStateInfo, cellStateInData);</div>
+<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
+<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <span class="keyword">auto</span> currentInputData = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(inputs[0]-&gt;Map());</div>
+<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputData = MakeDecoder&lt;float&gt;(lstmInputInfo, currentInputData);</div>
+<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keyword">auto</span> currentOutputData = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(outputs[2]-&gt;Map());</div>
+<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; output = MakeEncoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputDecoder = MakeDecoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
+<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToInputWeightsTensor;</div>
+<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToForgetWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        m_InputToForgetWeightsTensor-&gt;GetTensorInfo(), m_InputToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToCellWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        m_InputToCellWeightsTensor-&gt;GetTensorInfo(), m_InputToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToOutputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        m_InputToOutputWeightsTensor-&gt;GetTensorInfo(), m_InputToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToInputWeightsTensor;</div>
+<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToForgetWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        m_RecurrentToForgetWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToCellWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;        m_RecurrentToCellWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToOutputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;        m_RecurrentToOutputWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
+<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateBiasTensor;</div>
+<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateBiasTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        m_ForgetGateBiasTensor-&gt;GetTensorInfo(), m_ForgetGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellBiasTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        m_CellBiasTensor-&gt;GetTensorInfo(), m_CellBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateBiasTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        m_OutputGateBiasTensor-&gt;GetTensorInfo(), m_OutputGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToInputWeightsTensor;</div>
+<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToForgetWeightsTensor;</div>
+<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToOutputWeightsTensor;</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;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionWeightsTensor;</div>
+<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionBiasTensor;</div>
+<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160; </div>
+<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputLayerNormWeights;</div>
+<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetLayerNormWeights;</div>
+<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellLayerNormWeights;</div>
+<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputLayerNormWeights;</div>
+<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160; </div>
+<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    <span class="keywordflow">if</span> (useLayerNorm)</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;        <span class="keywordflow">if</span> (!useCifg)</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;            inputLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;                    m_InputLayerNormWeights-&gt;GetTensorInfo(), m_InputLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        }</div>
+<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        forgetLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;                m_ForgetLayerNormWeights-&gt;GetTensorInfo(), m_ForgetLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        cellLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;                m_CellLayerNormWeights-&gt;GetTensorInfo(), m_CellLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        outputLayerNormWeights = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                m_OutputLayerNormWeights-&gt;GetTensorInfo(), m_OutputLayerNormWeights-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    }</div>
+<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160; </div>
+<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    <span class="keywordflow">if</span> (!useCifg)</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;        inputToInputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;            m_InputToInputWeightsTensor-&gt;GetTensorInfo(), m_InputToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        inputGateBiasTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;            m_InputGateBiasTensor-&gt;GetTensorInfo(), m_InputGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        recurrentToInputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;            m_RecurrentToInputWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    }</div>
+<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
+<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    <span class="keywordflow">if</span> (usePeephole)</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;        cellToForgetWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;            m_CellToForgetWeightsTensor-&gt;GetTensorInfo(), m_CellToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        cellToOutputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;            m_CellToOutputWeightsTensor-&gt;GetTensorInfo(), m_CellToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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; </div>
+<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    <span class="keywordflow">if</span> (!useCifg &amp;&amp; usePeephole)</div>
+<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    {</div>
+<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        cellToInputWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;            m_CellToInputWeightsTensor-&gt;GetTensorInfo(), m_CellToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</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; </div>
+<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <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>&#160;    {</div>
+<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        projectionWeightsTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;            m_ProjectionWeightsTensor-&gt;GetTensorInfo(), m_ProjectionWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
+<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        {</div>
+<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;            projectionBiasTensor = MakeDecoder&lt;float&gt;(</div>
+<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;                m_ProjectionBiasTensor-&gt;GetTensorInfo(), m_ProjectionBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
+<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        }</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; </div>
+<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    <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>&#160;    <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>&#160; </div>
+<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> t = 0; t &lt; maxTime; ++t)</div>
+<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    {</div>
+<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        <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>&#160;                 lstmInputInfo,</div>
+<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;                 lstmOutputInfo,</div>
+<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                 inputToOutputWeightsShape,</div>
+<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;                 recurrentToOutputWeightsShape,</div>
+<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;                 inputData,</div>
+<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;                 outputStateIn,</div>
+<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;                 cellStateIn,</div>
+<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;                 outputStateOut,</div>
+<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                 cellStateOut,</div>
+<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                 output,</div>
+<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                 cellStateOutDecoder,</div>
+<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;                 outputDecoder,</div>
+<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;                 inputToInputWeightsTensor,</div>
+<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;                 inputToForgetWeightsTensor,</div>
+<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;                 inputToCellWeightsTensor,</div>
+<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;                 inputToOutputWeightsTensor,</div>
+<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                 recurrentToInputWeightsTensor,</div>
+<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;                 recurrentToForgetWeightsTensor,</div>
+<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;                 recurrentToCellWeightsTensor,</div>
+<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;                 recurrentToOutputWeightsTensor,</div>
+<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;                 cellToInputWeightsTensor,</div>
+<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;                 cellToForgetWeightsTensor,</div>
+<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;                 cellToOutputWeightsTensor,</div>
+<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;                 inputGateBiasTensor,</div>
+<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;                 forgetGateBiasTensor,</div>
+<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;                 cellBiasTensor,</div>
+<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;                 outputGateBiasTensor,</div>
+<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;                 projectionWeightsTensor,</div>
+<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;                 projectionBiasTensor,</div>
+<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;                 inputLayerNormWeights,</div>
+<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;                 forgetLayerNormWeights,</div>
+<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;                 cellLayerNormWeights,</div>
+<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;                 outputLayerNormWeights,</div>
+<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;                 inputGateScratch,</div>
+<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;                 cellScratch,</div>
+<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;                 forgetGateScratch,</div>
+<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                 outputGateScratch,</div>
+<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                 inputGateScratchDecoder,</div>
+<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                 cellScratchDecoder,</div>
+<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                 forgetGateScratchDecoder,</div>
+<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;                 outputGateScratchDecoder,</div>
+<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;                 m_LayerNormEpsilon);</div>
+<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
+<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        currentInputData += batchInputSize;</div>
+<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        inputData = MakeDecoder&lt;float&gt;(lstmInputInfo, currentInputData);</div>
+<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        currentOutputData += batchOutputSize;</div>
+<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        output = MakeEncoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        outputDecoder = MakeDecoder&lt;float&gt;(lstmOutputInfo, currentOutputData);</div>
+<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160; </div>
+<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <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>&#160;        outputStateIn = MakeDecoder&lt;float&gt;(outputStateInfo, outputStateOutData);</div>
+<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160; </div>
+<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        <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>&#160;        cellStateIn = MakeDecoder&lt;float&gt;(cellStateInfo, cellStateOutData);</div>
+<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    }</div>
+<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160; </div>
+<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <span class="keywordflow">if</span> (!<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>&#160;    {</div>
+<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        <span class="comment">// Permute Output back to batch major</span></div>
+<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        <span class="keyword">const</span> PermutationVector&amp; mappings = {1U, 0U, 2U};</div>
+<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        <span class="keyword">auto</span> outputData = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(outputs[2]-&gt;Map());</div>
+<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;        std::vector&lt;float&gt; 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>&#160;        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>&#160;        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>&#160;        <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>&#160;    }</div>
+<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;}</div>
+<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160; </div>
+<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;} <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&lt; armnn::ScopedTensorHandle &gt; 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 &amp;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 &amp;dstShape, const armnn::PermutationVector &amp;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 &amp;srcShape, const armnn::PermutationVector &amp;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 &amp;dstShape, const armnn::PermutationVector &amp;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 &amp;srcShape, const armnn::PermutationVector &amp;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 &amp;descriptor, const TensorInfo &amp;inputInfo, const TensorInfo &amp;outputInfo, const TensorShape &amp;inputToOutputWeightsShape, const TensorShape &amp;recurrentToOutputWeightsShape, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputData, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;outputStateIn, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellStateIn, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;outputStateOut, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;cellStateOut, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;output, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellStateOutDecoder, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;outputDecoder, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputToInputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputToForgetWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputToCellWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputToOutputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;recurrentToInputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;recurrentToForgetWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;recurrentToCellWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;recurrentToOutputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellToInputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellToForgetWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellToOutputWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputGateBiasTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;forgetGateBiasTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellBiasTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;outputGateBiasTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;projectionWeightsTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;projectionBiasTensor, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputLayerNormWeights, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;forgetLayerNormWeights, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellLayerNormWeights, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;outputLayerNormWeights, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;inputGateScratch, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;cellScratch, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;forgetGateScratch, std::unique_ptr&lt; Encoder&lt; float &gt;&gt; &amp;outputGateScratch, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;inputGateScratchDecoder, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;cellScratchDecoder, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;forgetGateScratchDecoder, std::unique_ptr&lt; Decoder&lt; float &gt;&gt; &amp;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 &amp;descriptor, const WorkloadInfo &amp;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 &amp; 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>
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 <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>
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+<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 &amp; forget gate).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01146">Descriptors.hpp:1146</a></div></div>
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+<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>
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 <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 &amp;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>
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-<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>
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@@ -456,7 +457,7 @@
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