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+<div class="header">
+  <div class="summary">
+<a href="#pub-methods">Public Member Functions</a> &#124;
+<a href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload-members.html">List of all members</a>  </div>
+  <div class="headertitle">
+<div class="title">NeonUnidirectionalSequenceLstmWorkload Class Reference</div>  </div>
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+<div class="contents">
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+<p><code>#include &lt;<a class="el" href="_neon_unidirectional_sequence_lstm_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmWorkload.hpp</a>&gt;</code></p>
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+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
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+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock">
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8hpp_source.html#l00022">22</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmWorkload.hpp</a>.</p>
+</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
+<a id="a39a30590f33a409c3c39181d446621aa"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a39a30590f33a409c3c39181d446621aa">&#9670;&nbsp;</a></span>NeonUnidirectionalSequenceLstmWorkload()</h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload.html">NeonUnidirectionalSequenceLstmWorkload</a> </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">UnidirectionalSequenceLstmQueueDescriptor</a> &amp;&#160;</td>
+          <td class="paramname"><em>descriptor</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.html#l00033">33</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmWorkload.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    : NeonBaseWorkload&lt;UnidirectionalSequenceLstmQueueDescriptor&gt;(descriptor, info)</div>
+<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;{</div>
+<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;    <span class="comment">// Report Profiling Details</span></div>
+<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    <a class="code" href="_profiling_8hpp.html#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">&quot;NeonUnidirectionalSequenceLstmWorkload_Construct&quot;</span>,</div>
+<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;                                         descriptor.m_Parameters,</div>
+<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;                                         info,</div>
+<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;                                         <a class="code" href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div>
+<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160; </div>
+<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    <span class="comment">// Input/Output tensors</span></div>
+<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    <span class="keyword">const</span> arm_compute::ITensor&amp; input         = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])-&gt;GetTensor();</div>
+<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    arm_compute::ITensor&amp; outputStateIn       = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])-&gt;GetTensor();</div>
+<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    <span class="keyword">const</span> arm_compute::ITensor&amp; cellStateIn   = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])-&gt;GetTensor();</div>
+<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160; </div>
+<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    arm_compute::ITensor&amp; outputStateOut = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0])-&gt;GetTensor();</div>
+<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    arm_compute::ITensor&amp; cellStateOut   = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1])-&gt;GetTensor();</div>
+<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    arm_compute::ITensor&amp; output         = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])-&gt;GetTensor();</div>
+<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
+<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    TensorInfo inputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_InputTensorInfos[0];</div>
+<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    TensorInfo outputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_OutputTensorInfos[2];</div>
+<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
+<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    TensorShape inputLayerShape = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])-&gt;GetShape();</div>
+<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    TensorShape outputLayerShape = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])-&gt;GetShape();</div>
+<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160; </div>
+<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = <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> ? inputLayerShape[0] : inputLayerShape[1];</div>
+<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <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> ? inputLayerShape[1] : inputLayerShape[0];</div>
+<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputLayerShape[2];</div>
+<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div>
+<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160; </div>
+<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keyword">const</span> TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});</div>
+<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    <span class="keyword">const</span> TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});</div>
+<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160; </div>
+<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <span class="comment">// Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.</span></div>
+<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00068"></a><span class="lineno">   68</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="l00069"></a><span class="lineno">   69</span>&#160;    {</div>
+<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        std::unique_ptr&lt;arm_compute::NEPermute&gt; layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
+<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160; </div>
+<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;        TensorInfo permuteOutInfo = inputInfo;</div>
+<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        permuteOutInfo.SetShape(timeMajorShapeInput);</div>
+<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);</div>
+<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);</div>
+<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160; </div>
+<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        <span class="comment">// Permute to time major format.</span></div>
+<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        layer-&gt;configure(&amp;input, &amp;m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));</div>
+<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        m_Permute1.reset(layer.release());</div>
+<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    }</div>
+<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
+<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <span class="comment">// Split and Concat Tensors</span></div>
+<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; maxTime; ++i)</div>
+<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    {</div>
+<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        arm_compute::Tensor splitter_out;</div>
+<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        arm_compute::Tensor concat_in;</div>
+<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
+<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <span class="keyword">auto</span> splitterTensorInfo = inputInfo;</div>
+<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="keyword">auto</span> concatTensorInfo = outputInfo;</div>
+<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        splitterTensorInfo.SetShape({batchSize, inputSize});</div>
+<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        concatTensorInfo.SetShape({batchSize, outputSize});</div>
+<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        BuildArmComputeTensor(splitter_out, splitterTensorInfo);</div>
+<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        BuildArmComputeTensor(concat_in, concatTensorInfo);</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;        armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);</div>
+<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);</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="comment">// append to std::vector&lt;arm_compute::Tensor&gt;</span></div>
+<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        m_SplitterOutputsTensors.push_back(std::move(splitter_out));</div>
+<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        m_ConcatInputsTensors.push_back(std::move(concat_in));</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; </div>
+<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; maxTime; ++i)</div>
+<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    {</div>
+<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="comment">// append to std::vector&lt;arm_compute::ITensor*&gt;</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        m_SplitterOutputs.push_back(&amp;m_SplitterOutputsTensors[i]);</div>
+<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        m_ConcatInputs.push_back(&amp;m_ConcatInputsTensors[i]);</div>
+<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    }</div>
+<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
+<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="comment">// Split</span></div>
+<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div>
+<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0; <span class="comment">// splitting on 0-dimension (i.e. maxTime dimension)</span></div>
+<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160; </div>
+<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL split does not work with only one element to split.</span></div>
+<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    {</div>
+<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        ViewsDescriptor splitterDesc(maxTime, numberDimensions);</div>
+<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[3] = {1, batchSize, inputSize};</div>
+<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIdx = 0u; outputIdx &lt; maxTime; ++outputIdx)</div>
+<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        {</div>
+<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;            splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);</div>
+<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0u; dimIdx &lt; numberDimensions; ++dimIdx)</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;                splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);</div>
+<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            }</div>
+<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        }</div>
+<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
+<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        std::set&lt;unsigned int&gt; splitAxis = <a class="code" href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">ComputeSplitAxis</a>(splitterDesc, timeMajorShapeInput);</div>
+<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160; </div>
+<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        std::unique_ptr&lt;arm_compute::NESplit&gt; split_layer(<span class="keyword">new</span> arm_compute::NESplit());</div>
+<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>                          aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(),</div>
+<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                                                                         *splitAxis.begin());</div>
+<div class="line"><a name="l00136"></a><span class="lineno">  136</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="l00137"></a><span class="lineno">  137</span>&#160;        {</div>
+<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;            split_layer-&gt;configure(&amp;m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);</div>
+<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;        } <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        {</div>
+<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;            split_layer-&gt;configure(&amp;input, m_SplitterOutputs, aclAxisSplit);</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; </div>
+<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        split_layer-&gt;prepare();</div>
+<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;        m_Splitter.reset(split_layer.release());</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; </div>
+<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="comment">// Lstm</span></div>
+<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    arm_compute::LSTMParams&lt;arm_compute::ITensor&gt; lstm_param;</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;    lstm_param.set_cell_clip_params(descriptor.m_Parameters.m_ClippingThresCell);</div>
+<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    lstm_param.set_projection_clip_params(descriptor.m_Parameters.m_ClippingThresProj);</div>
+<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160; </div>
+<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    lstm_param.set_matmul_scale_params(descriptor.m_Parameters.m_InputIntermediateScale,</div>
+<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;                                       descriptor.m_Parameters.m_ForgetIntermediateScale,</div>
+<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;                                       descriptor.m_Parameters.m_CellIntermediateScale,</div>
+<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;                                       descriptor.m_Parameters.m_OutputIntermediateScale);</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;    lstm_param.set_hidden_state_params(descriptor.m_Parameters.m_HiddenStateZeroPoint,</div>
+<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                                       descriptor.m_Parameters.m_HiddenStateScale);</div>
+<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
+<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    m_InputToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aab77f54a037658ca9b2bf9cc8a1fadf1">m_InputToForgetWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
+<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    m_InputToCellWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a841439e3b8dc157a7368b19c9ecb7d03">m_InputToCellWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160; </div>
+<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    m_InputToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
+<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    m_RecurrentToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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;    m_RecurrentToCellWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ac18c8b8b2039267d8282e91b4162d8aa">m_RecurrentToCellWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160; </div>
+<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    m_RecurrentToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a4c27716f61bb68e8ea0bd4e8389ba01a">m_RecurrentToOutputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160; </div>
+<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    m_ForgetGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a29fa293fffbf9c6f00cd75db1dc0a52a">m_ForgetGateBias</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160; </div>
+<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    m_CellBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a6e9593869b82984de198fed27f72cdcf">m_CellBias</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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;    m_OutputGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a5ff4158b1b363b898d0da04c42d37ce0">m_OutputGateBias</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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="comment">// for future reference: check the AndroidNN API for the logic here</span></div>
+<div class="line"><a name="l00192"></a><span class="lineno">  192</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#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div>
+<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    {</div>
+<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        m_InputToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ab160eba2493d5fe52185c0986dcb190c">m_InputToInputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160; </div>
+<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        m_RecurrentToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a299587d4f3fca029492700f3e2585bd8">m_RecurrentToInputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160; </div>
+<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        m_CellToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00201"></a><span class="lineno">  201</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_unidirectional_sequence_lstm_queue_descriptor.html#a951b7c90b862138071a298065f16be61">m_CellToInputWeights</a> != <span class="keyword">nullptr</span>)</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;            BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a951b7c90b862138071a298065f16be61">m_CellToInputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        }</div>
+<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
+<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        m_InputGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a44eb7524badcca9b2073359e3814c98b">m_InputGateBias</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div>
+<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;                                   m_RecurrentToInputWeightsTensor.get(),</div>
+<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;                                   <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a951b7c90b862138071a298065f16be61">m_CellToInputWeights</a> ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div>
+<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;                                   m_InputGateBiasTensor.get());</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> (<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="l00215"></a><span class="lineno">  215</span>&#160;    {</div>
+<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        m_ProjectionWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a3ead2ef8da00b2709d561d85996fc513">m_ProjectionWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
+<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        m_ProjectionBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00220"></a><span class="lineno">  220</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_unidirectional_sequence_lstm_queue_descriptor.html#ac668b31de6fb0f19d4c793d5ed3c3316">m_ProjectionBias</a> != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        {</div>
+<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;            BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ac668b31de6fb0f19d4c793d5ed3c3316">m_ProjectionBias</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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; </div>
+<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div>
+<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                                         <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ac668b31de6fb0f19d4c793d5ed3c3316">m_ProjectionBias</a> ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</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; </div>
+<div class="line"><a name="l00229"></a><span class="lineno">  229</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#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>)</div>
+<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    {</div>
+<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        m_CellToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a20c10fcb26657477377d07b7b1e13120">m_CellToForgetWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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;        m_CellToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aa09f7bdb9fd0d06b6386e412a4e72dd6">m_CellToOutputWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</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;        lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div>
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+<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
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+<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;        m_InputLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00243"></a><span class="lineno">  243</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#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div>
+<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        {</div>
+<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;            BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a1dbad32cad5c0437e1272f59fedf52ea">m_InputLayerNormWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        }</div>
+<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160; </div>
+<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        m_ForgetLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ad9442e26aa79f896da5f404ab825a9c8">m_ForgetLayerNormWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160; </div>
+<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        m_CellLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aeef6f1ac3efad8ec8b0a7118652b64c9">m_CellLayerNormWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160; </div>
+<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        m_OutputLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
+<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a0e0f66bd03c88f3d2dc666f581d3cf12">m_OutputLayerNormWeights</a>-&gt;<a class="code" href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>());</div>
+<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160; </div>
+<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="keyword">auto</span> inputNormWeightTensor = <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> ? nullptr : m_InputLayerNormWeightsTensor.get();</div>
+<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        lstm_param.set_layer_normalization_params(inputNormWeightTensor,</div>
+<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;                                                  m_ForgetLayerNormWeightsTensor.get(),</div>
+<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;                                                  m_CellLayerNormWeightsTensor.get(),</div>
+<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                                                  m_OutputLayerNormWeightsTensor.get());</div>
+<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    }</div>
+<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
+<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i != maxTime; ++i)</div>
+<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    {</div>
+<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        <span class="comment">// Set LSTM input and output ITensors depending on:</span></div>
+<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        <span class="comment">// input format (timeMajor) &amp; number of LSTM batches (maxTime).</span></div>
+<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        arm_compute::ITensor* outputLSTM;</div>
+<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;        arm_compute::ITensor* inputLSTM;</div>
+<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160; </div>
+<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <span class="comment">// If there is only one LSTM time major batch, we will not concat OR permute.</span></div>
+<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        <span class="comment">// Set input of LSTM to be first input ITensor.</span></div>
+<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="comment">// Set output of LSTM to be final output ITensor.</span></div>
+<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        <span class="comment">// LSTM input/output cannot be &gt; 2 dimensions so need to resize its TensorInfo.</span></div>
+<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; <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="l00276"></a><span class="lineno">  276</span>&#160;        {</div>
+<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;            TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(input.info()-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;            TensorShape outputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(output.info()-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160; </div>
+<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;            TensorShape outputShapeShrink({outputShape[1], outputShape[2]});</div>
+<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
+<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;            <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
+<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;            <span class="keyword">auto</span> acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);</div>
+<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160; </div>
+<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;            input.info()-&gt;set_tensor_shape(acl_input_shape_shrink);</div>
+<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;            inputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(&amp;input);</div>
+<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
+<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;            output.info()-&gt;set_tensor_shape(acl_output_shape_shrink);</div>
+<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;            outputLSTM = &amp;output;</div>
+<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        }</div>
+<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        <span class="comment">// If there is only one LSTM batch major batch, we will not concat, only permute.</span></div>
+<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;        <span class="comment">// Set input of LSTM to be output of initial permute.</span></div>
+<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <span class="comment">// Set output of LSTM to be first element of m_ConcatInputs &amp; use that value later in permute.</span></div>
+<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        <span class="comment">// LSTM output cannot be &gt; 2 dimensions so need to resize its TensorInfo.</span></div>
+<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; !<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="l00297"></a><span class="lineno">  297</span>&#160;        {</div>
+<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;            TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_PermuteFirstOut.info()-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;            <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
+<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;            m_PermuteFirstOut.info()-&gt;set_tensor_shape(acl_input_shape_shrink);</div>
+<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;            inputLSTM = &amp;m_PermuteFirstOut;</div>
+<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160; </div>
+<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;            outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(m_ConcatInputs[i]);</div>
+<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        }</div>
+<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;        <span class="comment">// Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.</span></div>
+<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        {</div>
+<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;            inputLSTM = m_SplitterOutputs[i];</div>
+<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;            outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(m_ConcatInputs[i]);</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;        std::unique_ptr&lt;arm_compute::NEQLSTMLayer&gt; lstm_layer(<span class="keyword">new</span> arm_compute::NEQLSTMLayer());</div>
+<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160; </div>
+<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;        lstm_layer-&gt;configure(inputLSTM,</div>
+<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                              m_InputToForgetWeightsTensor.get(),</div>
+<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;                              m_InputToCellWeightsTensor.get(),</div>
+<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;                              m_InputToOutputWeightsTensor.get(),</div>
+<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;                              m_RecurrentToForgetWeightsTensor.get(),</div>
+<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;                              m_RecurrentToCellWeightsTensor.get(),</div>
+<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;                              m_RecurrentToOutputWeightsTensor.get(),</div>
+<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;                              m_ForgetGateBiasTensor.get(),</div>
+<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;                              m_CellBiasTensor.get(),</div>
+<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;                              m_OutputGateBiasTensor.get(),</div>
+<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;                              &amp;cellStateIn,</div>
+<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;                              &amp;outputStateIn,</div>
+<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                              &amp;cellStateOut,</div>
+<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;                              &amp;outputStateOut,</div>
+<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;                              outputLSTM,</div>
+<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;                              lstm_param);</div>
+<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
+<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;        m_Layers.emplace_back(std::move(lstm_layer));</div>
+<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    }</div>
+<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160; </div>
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+<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a17ba1c8bcc71a55a95b2a3913f8cb203">m_InputToOutputWeights</a>);</div>
+<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#adf8571dd1867ee91082bd005f94f2610">m_RecurrentToForgetWeights</a>);</div>
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+<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    }</div>
+<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div>
+<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div>
+<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    {</div>
+<div class="line"><a name="l00434"></a><span class="lineno">  434</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="l00435"></a><span class="lineno">  435</span>&#160;        {</div>
+<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;            output.info()-&gt;set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));</div>
+<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        }</div>
+<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        {</div>
+<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;            output.info()-&gt;set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
+<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        }</div>
+<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    }</div>
+<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160; </div>
+<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    <span class="comment">// Permute: only done if input/output are in batch major format.</span></div>
+<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00447"></a><span class="lineno">  447</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="l00448"></a><span class="lineno">  448</span>&#160;    {</div>
+<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        <span class="comment">// Output now time major. Permute output back to batch major.</span></div>
+<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        std::unique_ptr&lt;arm_compute::NEPermute&gt; layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
+<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        <span class="keywordflow">if</span> (maxTime != 1)</div>
+<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        {</div>
+<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;            layer-&gt;configure(&amp;concat_out, &amp;output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        }</div>
+<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;        {</div>
+<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;            layer-&gt;configure(m_ConcatInputs[0], &amp;output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        }</div>
+<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        m_Permute2.reset(layer.release());</div>
+<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    }</div>
+<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160; </div>
+<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    FreeUnusedTensors();</div>
+<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;}</div>
+</div><!-- fragment -->
+<p class="reference">References <a class="el" href="_profiling_8hpp_source.html#l00227">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>, <a class="el" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, and <a class="el" href="_workload_data_8hpp_source.html#l00066">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>.</p>
+
+</div>
+</div>
+<h2 class="groupheader">Member Function Documentation</h2>
+<a id="ae071e8822437c78baea75c3aef3a263a"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae071e8822437c78baea75c3aef3a263a">&#9670;&nbsp;</a></span>Execute()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname">void Execute </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td> const</td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Implements <a class="el" href="classarmnn_1_1_i_workload.html#a72ae00e6604850c8798c5e0d825ee7e4">IWorkload</a>.</p>
+
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.html#l00465">465</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmWorkload.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;{</div>
+<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    <a class="code" href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>(<span class="stringliteral">&quot;NeonUnidirectionalSequenceLstmWorkload_Execute&quot;</span>);</div>
+<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <span class="keywordflow">if</span> (m_Permute1)</div>
+<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    {</div>
+<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        m_Permute1-&gt;run();</div>
+<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    }</div>
+<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    <span class="keywordflow">if</span> (m_Splitter)</div>
+<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    {</div>
+<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;        m_Splitter-&gt;run();</div>
+<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    }</div>
+<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    <span class="keywordflow">for</span> (uint32_t i = 0; i &lt; m_Layers.size(); ++i)</div>
+<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    {</div>
+<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        m_Layers[i]-&gt;run();</div>
+<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    }</div>
+<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    <span class="keywordflow">if</span> (m_Concat)</div>
+<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    {</div>
+<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;        m_Concat-&gt;run();</div>
+<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    }</div>
+<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    <span class="keywordflow">if</span> (m_Permute2)</div>
+<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    {</div>
+<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        m_Permute2-&gt;run();</div>
+<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    }</div>
+<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;}</div>
+</div><!-- fragment -->
+<p class="reference">References <a class="el" href="_neon_workload_utils_8hpp_source.html#l00032">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>.</p>
+
+</div>
+</div>
+<hr/>The documentation for this class was generated from the following files:<ul>
+<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmWorkload.hpp</a></li>
+<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmWorkload.cpp</a></li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a6e9593869b82984de198fed27f72cdcf"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a6e9593869b82984de198fed27f72cdcf">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellBias</a></div><div class="ttdeci">const ConstTensorHandle * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00736">WorkloadData.hpp:736</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_ac18c8b8b2039267d8282e91b4162d8aa"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ac18c8b8b2039267d8282e91b4162d8aa">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00729">WorkloadData.hpp:729</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="anamespacearmnn_html_a7863c179ff92feec660c48ab7b95ae55"><div class="ttname"><a href="namespacearmnn.html#a7863c179ff92feec660c48ab7b95ae55">armnn::ConcatDescriptor</a></div><div class="ttdeci">OriginsDescriptor ConcatDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_fwd_8hpp_source.html#l00059">DescriptorsFwd.hpp:59</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a1dbad32cad5c0437e1272f59fedf52ea"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a1dbad32cad5c0437e1272f59fedf52ea">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00740">WorkloadData.hpp:740</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a17ba1c8bcc71a55a95b2a3913f8cb203"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a17ba1c8bcc71a55a95b2a3913f8cb203">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00726">WorkloadData.hpp:726</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a0e0f66bd03c88f3d2dc666f581d3cf12"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a0e0f66bd03c88f3d2dc666f581d3cf12">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00743">WorkloadData.hpp:743</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_const_tensor_handle_html_a66e8f43a5b42b500871ed96e15419567"><div class="ttname"><a href="classarmnn_1_1_const_tensor_handle.html#a66e8f43a5b42b500871ed96e15419567">armnn::ConstTensorHandle::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo &amp; GetTensorInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_8hpp_source.html#l00040">TensorHandle.hpp:40</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a8cbabc875597b3bed0ccdc0adb289fde"><div class="ttname"><a href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">armnn::ComputeSplitAxis</a></div><div class="ttdeci">std::set&lt; unsigned int &gt; ComputeSplitAxis(const armnn::SplitterDescriptor &amp;desc, const TensorShape &amp;input)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00246">ArmComputeUtils.hpp:246</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a5ff4158b1b363b898d0da04c42d37ce0"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a5ff4158b1b363b898d0da04c42d37ce0">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00737">WorkloadData.hpp:737</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_ad9442e26aa79f896da5f404ab825a9c8"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ad9442e26aa79f896da5f404ab825a9c8">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00741">WorkloadData.hpp:741</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_aab77f54a037658ca9b2bf9cc8a1fadf1"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aab77f54a037658ca9b2bf9cc8a1fadf1">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00724">WorkloadData.hpp:724</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a4c27716f61bb68e8ea0bd4e8389ba01a"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a4c27716f61bb68e8ea0bd4e8389ba01a">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00730">WorkloadData.hpp:730</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a611208865d55ea576cc89ac86d7c19b7"><div class="ttname"><a href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">armnn::InitializeArmComputeTensorData</a></div><div class="ttdeci">void InitializeArmComputeTensorData(arm_compute::Tensor &amp;tensor, TensorInfo tensorInfo, const ITensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.html#l00068">NeonWorkloadUtils.hpp:68</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a3ead2ef8da00b2709d561d85996fc513"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a3ead2ef8da00b2709d561d85996fc513">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00738">WorkloadData.hpp:738</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="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>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a29fa293fffbf9c6f00cd75db1dc0a52a"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a29fa293fffbf9c6f00cd75db1dc0a52a">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00735">WorkloadData.hpp:735</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a44eb7524badcca9b2073359e3814c98b"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a44eb7524badcca9b2073359e3814c98b">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputGateBias</a></div><div class="ttdeci">const ConstTensorHandle * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00734">WorkloadData.hpp:734</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a951b7c90b862138071a298065f16be61"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a951b7c90b862138071a298065f16be61">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00731">WorkloadData.hpp:731</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_aa09f7bdb9fd0d06b6386e412a4e72dd6"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aa09f7bdb9fd0d06b6386e412a4e72dd6">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00733">WorkloadData.hpp:733</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&lt; ITensorHandle * &gt; 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="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_adf8571dd1867ee91082bd005f94f2610"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#adf8571dd1867ee91082bd005f94f2610">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00728">WorkloadData.hpp:728</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a20c10fcb26657477377d07b7b1e13120"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a20c10fcb26657477377d07b7b1e13120">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00732">WorkloadData.hpp:732</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_a299587d4f3fca029492700f3e2585bd8"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a299587d4f3fca029492700f3e2585bd8">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00727">WorkloadData.hpp:727</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_ab160eba2493d5fe52185c0986dcb190c"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ab160eba2493d5fe52185c0986dcb190c">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00723">WorkloadData.hpp:723</a></div></div>
+<div class="ttc" id="a_profiling_8hpp_html_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.html#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a></div><div class="ttdeci">#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.html#l00227">Profiling.hpp:227</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_base_workload_html_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload&lt; UnidirectionalSequenceLstmQueueDescriptor &gt;::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00067">Workload.hpp:67</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 &amp; 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&lt; UnidirectionalSequenceLstmQueueDescriptor &gt;::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="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="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_ac668b31de6fb0f19d4c793d5ed3c3316"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#ac668b31de6fb0f19d4c793d5ed3c3316">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensorHandle * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00739">WorkloadData.hpp:739</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_a841439e3b8dc157a7368b19c9ecb7d03"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#a841439e3b8dc157a7368b19c9ecb7d03">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00725">WorkloadData.hpp:725</a></div></div>
+<div class="ttc" id="a_neon_workload_utils_8hpp_html_a7f97eedf3c9436b110df92c947bbb55d"><div class="ttname"><a href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_NEON_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="_neon_workload_utils_8hpp_source.html#l00032">NeonWorkloadUtils.hpp:32</a></div></div>
+<div class="ttc" id="anamespacearmnn_utils_html_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.html#l00021">TensorUtils.cpp:21</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_html_aeef6f1ac3efad8ec8b0a7118652b64c9"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html#aeef6f1ac3efad8ec8b0a7118652b64c9">armnn::UnidirectionalSequenceLstmQueueDescriptor::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensorHandle * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00742">WorkloadData.hpp:742</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00026">WorkloadData.hpp:26</a></div></div>
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