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+<div class="title">NeonUnidirectionalSequenceLstmWorkload.cpp</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a href="_neon_unidirectional_sequence_lstm_workload_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">//</span></div>
+<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div>
+<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div>
+<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div>
+<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160; </div>
+<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_neon_unidirectional_sequence_lstm_workload_8hpp.xhtml">NeonUnidirectionalSequenceLstmWorkload.hpp</a>&quot;</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.hpp</a>&quot;</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160; </div>
+<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_utils_8hpp.xhtml">aclCommon/ArmComputeUtils.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_tensor_utils_8hpp.xhtml">aclCommon/ArmComputeTensorUtils.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160; </div>
+<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;neon/test/NeonWorkloadFactoryHelper.hpp&gt;</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_utils_8hpp.xhtml">backendsCommon/WorkloadUtils.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160; </div>
+<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_neon_tensor_handle_8hpp.xhtml">neon/NeonTensorHandle.hpp</a>&quot;</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160; </div>
+<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="keyword">namespace</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;{</div>
+<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160; </div>
+<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> CalcAclAxis(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axis)</div>
+<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;{</div>
+<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <span class="keywordflow">return</span> (numDimensions - axis) - 1;</div>
+<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;}</div>
+<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;} <span class="comment">//namespace</span></div>
+<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160; </div>
+<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div>
+<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;{</div>
+<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="keyword">using namespace </span>armcomputetensorutils;</div>
+<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160; </div>
+<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<a class="code" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload.xhtml#a39a30590f33a409c3c39181d446621aa">NeonUnidirectionalSequenceLstmWorkload::NeonUnidirectionalSequenceLstmWorkload</a></div>
+<div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload.xhtml#a39a30590f33a409c3c39181d446621aa">   33</a></span>&#160;    (<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">UnidirectionalSequenceLstmQueueDescriptor</a>&amp; descriptor, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; info)</div>
+<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    : <a class="code" href="classarmnn_1_1_neon_base_workload.xhtml">NeonBaseWorkload&lt;UnidirectionalSequenceLstmQueueDescriptor&gt;</a>(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.xhtml#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.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div>
+<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;                                         <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div>
+<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;                                         GetGuid());</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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Inputs[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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Inputs[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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Inputs[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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Outputs[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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Outputs[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><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Outputs[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;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo = <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_InputTensorInfos[0];</div>
+<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = <a class="code" href="namespacearmnn.xhtml#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;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputLayerShape = <span class="keyword">static_cast&lt;</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Inputs[0])-&gt;GetShape();</div>
+<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputLayerShape = <span class="keyword">static_cast&lt;</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">&gt;</span>(m_Data.m_Outputs[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 = m_Data.m_Parameters.m_TimeMajor ? 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 = m_Data.m_Parameters.m_TimeMajor ? 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> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeInput({maxTime, batchSize, inputSize});</div>
+<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> 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> (!m_Data.m_Parameters.m_TimeMajor)</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;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteOutInfo = inputInfo;</div>
+<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        permuteOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(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.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({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;        <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> 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.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(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.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(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.xhtml#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.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">GetNumDimensions</a>(),</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> (!m_Data.m_Parameters.m_TimeMajor)</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.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a>);</div>
+<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    lstm_param.set_projection_clip_params(descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a>);</div>
+<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160; </div>
+<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    lstm_param.set_matmul_scale_params(descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>,</div>
+<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;                                       descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a>,</div>
+<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;                                       descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>,</div>
+<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;                                       descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a>);</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.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a>,</div>
+<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                                       descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>);</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, m_Data.m_InputToForgetWeights-&gt;GetTensorInfo());</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, m_Data.m_InputToCellWeights-&gt;GetTensorInfo());</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, m_Data.m_InputToOutputWeights-&gt;GetTensorInfo());</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, m_Data.m_RecurrentToForgetWeights-&gt;GetTensorInfo());</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, m_Data.m_RecurrentToCellWeights-&gt;GetTensorInfo());</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, m_Data.m_RecurrentToOutputWeights-&gt;GetTensorInfo());</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, m_Data.m_ForgetGateBias-&gt;GetTensorInfo());</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, m_Data.m_CellBias-&gt;GetTensorInfo());</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, m_Data.m_OutputGateBias-&gt;GetTensorInfo());</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> (!m_Data.m_Parameters.m_CifgEnabled)</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, m_Data.m_InputToInputWeights-&gt;GetTensorInfo());</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, m_Data.m_RecurrentToInputWeights-&gt;GetTensorInfo());</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> (m_Data.m_CellToInputWeights != <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, m_Data.m_CellToInputWeights-&gt;GetTensorInfo());</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, m_Data.m_InputGateBias-&gt;GetTensorInfo());</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;                                   m_Data.m_CellToInputWeights ? 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> (m_Data.m_Parameters.m_ProjectionEnabled)</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, m_Data.m_ProjectionWeights-&gt;GetTensorInfo());</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> (m_Data.m_ProjectionBias != <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, m_Data.m_ProjectionBias-&gt;GetTensorInfo());</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;                                         m_Data.m_ProjectionBias ? 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> (m_Data.m_Parameters.m_PeepholeEnabled)</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, m_Data.m_CellToForgetWeights-&gt;GetTensorInfo());</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, m_Data.m_CellToOutputWeights-&gt;GetTensorInfo());</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>
+<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; </div>
+<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    <span class="keywordflow">if</span> (m_Data.m_Parameters.m_LayerNormEnabled)</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;        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> (!m_Data.m_Parameters.m_CifgEnabled)</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, m_Data.m_InputLayerNormWeights-&gt;GetTensorInfo());</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, m_Data.m_ForgetLayerNormWeights-&gt;GetTensorInfo());</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, m_Data.m_CellLayerNormWeights-&gt;GetTensorInfo());</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, m_Data.m_OutputLayerNormWeights-&gt;GetTensorInfo());</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 = m_Data.m_Parameters.m_CifgEnabled ? 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; m_Data.m_Parameters.m_TimeMajor)</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;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(input.info()-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="namespacearmnn_utils.xhtml#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;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> 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; !m_Data.m_Parameters.m_TimeMajor)</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;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_PermuteFirstOut.info()-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> 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>
+<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights);</div>
+<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights);</div>
+<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights);</div>
+<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights);</div>
+<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights);</div>
+<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights);</div>
+<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias);</div>
+<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellBiasTensor, m_Data.m_CellBias);</div>
+<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias);</div>
+<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; </div>
+<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    {</div>
+<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights);</div>
+<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights);</div>
+<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keywordflow">if</span> (m_Data.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;        {</div>
+<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights);</div>
+<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;        }</div>
+<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputGateBiasTensor, m_Data.m_InputGateBias);</div>
+<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    }</div>
+<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160; </div>
+<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    <span class="keywordflow">if</span> (m_Data.m_Parameters.m_ProjectionEnabled)</div>
+<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    {</div>
+<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights);</div>
+<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;        <span class="keywordflow">if</span> (m_Data.m_ProjectionBias != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;        {</div>
+<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias);</div>
+<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;        }</div>
+<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    }</div>
+<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160; </div>
+<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <span class="keywordflow">if</span> (m_Data.m_Parameters.m_PeepholeEnabled)</div>
+<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    {</div>
+<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights);</div>
+<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights);</div>
+<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    }</div>
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+<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</div>
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+<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div>
+<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160; </div>
+<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeInput({maxTime, batchSize, inputSize});</div>
+<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeOutput({maxTime, batchSize, outputSize});</div>
+<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160; </div>
+<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusPermute1 = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;                                                             <span class="stringliteral">&quot;Permute1 status&quot;</span>);</div>
+<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusSplit = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;                                                          <span class="stringliteral">&quot;Split status&quot;</span>);</div>
+<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusLSTM = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;                                                         <span class="stringliteral">&quot;LSTM status&quot;</span>);</div>
+<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusConcat = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;                                                           <span class="stringliteral">&quot;Concat status&quot;</span>);</div>
+<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusPermute2 = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;                                                             <span class="stringliteral">&quot;Permute2 status&quot;</span>);</div>
+<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160; </div>
+<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclInputInfo  = armcomputetensorutils::BuildArmComputeTensorInfo(input);</div>
+<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclOutputInfo  = armcomputetensorutils::BuildArmComputeTensorInfo(output);</div>
+<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160; </div>
+<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    <span class="comment">// Permute validate</span></div>
+<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteOutInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(input);</div>
+<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo);</div>
+<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    {</div>
+<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        statusPermute1 =  arm_compute::NEPermute::validate(&amp;aclInputInfo,</div>
+<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;                                                           &amp;aclPermuteOutInfo,</div>
+<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;                                                           arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    }</div>
+<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160; </div>
+<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    <span class="comment">// Split and Concat Tensors validate</span></div>
+<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    std::vector&lt;arm_compute::TensorInfo&gt; splitterOutputsTensorInfos;</div>
+<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    std::vector&lt;arm_compute::TensorInfo&gt; concatInputsTensorInfos;</div>
+<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;    std::vector&lt;arm_compute::ITensorInfo*&gt; splitterOutputsTensorInfosPtr;</div>
+<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    std::vector&lt;const arm_compute::ITensorInfo*&gt; concatInputsTensorInfosPtr;</div>
+<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    splitterOutputsTensorInfos.reserve(maxTime);</div>
+<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    concatInputsTensorInfos.reserve(maxTime);</div>
+<div class="line"><a name="l00546"></a><span class="lineno">  546</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="l00547"></a><span class="lineno">  547</span>&#160;    {</div>
+<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        arm_compute::TensorInfo splitter_out;</div>
+<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;        arm_compute::TensorInfo concat_in;</div>
+<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160; </div>
+<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;        <span class="keyword">auto</span> splitterTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(input);</div>
+<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;        <span class="keyword">auto</span> concatTensorInfo   = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(output);</div>
+<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;        splitterTensorInfo.SetShape({batchSize, inputSize});</div>
+<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;        concatTensorInfo.SetShape({batchSize, outputSize});</div>
+<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160; </div>
+<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;        arm_compute::TensorInfo aclSplitterTensorInfo</div>
+<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;            = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo);</div>
+<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;        arm_compute::TensorInfo aclConcatTensorInfo</div>
+<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;            = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo);</div>
+<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160; </div>
+<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;        splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo);</div>
+<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;        concatInputsTensorInfos.emplace_back(aclConcatTensorInfo);</div>
+<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;        splitterOutputsTensorInfosPtr.emplace_back(&amp;splitterOutputsTensorInfos[i]);</div>
+<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;        concatInputsTensorInfosPtr.emplace_back(&amp;concatInputsTensorInfos[i]);</div>
+<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    }</div>
+<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160; </div>
+<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;    <span class="comment">// Split validate</span></div>
+<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div>
+<div class="line"><a name="l00571"></a><span class="lineno">  571</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="l00572"></a><span class="lineno">  572</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisSplit = CalcAclAxis(numberDimensions, dimension);</div>
+<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160; </div>
+<div class="line"><a name="l00574"></a><span class="lineno">  574</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="l00575"></a><span class="lineno">  575</span>&#160;    {</div>
+<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;        <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;        {</div>
+<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;            statusSplit = arm_compute::NESplit::validate(&amp;aclPermuteOutInfo,</div>
+<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;                                                         splitterOutputsTensorInfosPtr,</div>
+<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;                                                         aclAxisSplit);</div>
+<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;        } <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;        {</div>
+<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;            statusSplit = arm_compute::NESplit::validate(&amp;aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit);</div>
+<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;        }</div>
+<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    }</div>
+<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160; </div>
+<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;    <span class="comment">// LSTM validate</span></div>
+<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160; </div>
+<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    arm_compute::LSTMParams&lt;arm_compute::ITensorInfo&gt; lstm_params_info;</div>
+<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160; </div>
+<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; scratchBuffer = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(cellStateIn.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div>
+<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160; </div>
+<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    lstm_params_info.set_cell_clip_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a>);</div>
+<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    lstm_params_info.set_projection_clip_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a>);</div>
+<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    <span class="comment">// The inputs and outputs</span></div>
+<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);</div>
+<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);</div>
+<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);</div>
+<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);</div>
+<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);</div>
+<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160; </div>
+<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;    <span class="comment">// Basic parameters</span></div>
+<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclInputToForgetWeightsInfo</div>
+<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a7dac08f19a1b235d5256d39136848a09">GetInputToForgetWeights</a>());</div>
+<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclInputToCellWeightsInfo</div>
+<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a3b3c26330a05bf4ea40f8a6b402be354">GetInputToCellWeights</a>());</div>
+<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclInputToOutputWeightsInfo</div>
+<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a800adf0f61e84d706060f63037c1a336">GetInputToOutputWeights</a>());</div>
+<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo</div>
+<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a534af7e4f3a6d50a6dab05abc245133d">GetRecurrentToForgetWeights</a>());</div>
+<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToCellWeightsInfo</div>
+<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae5bfdd423b16f990c1713ef9f91f947b">GetRecurrentToCellWeights</a>());</div>
+<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo</div>
+<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#afe4d25acd31b98dee6f6b28d4d756071">GetRecurrentToOutputWeights</a>());</div>
+<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclForgetGateBiasInfo</div>
+<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ac81393ef433b0c7c337f9f0d55f41ae4">GetForgetGateBias</a>());</div>
+<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclCellBiasInfo</div>
+<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ad5f4be37766b41f342dd196cb1c6e141">GetCellBias</a>());</div>
+<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    <span class="keyword">const</span> arm_compute::TensorInfo aclOutputGateBiasInfo</div>
+<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;                                      = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae0da94ba17ce67b95b5b9d6e5adc4271">GetOutputGateBias</a>());</div>
+<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160; </div>
+<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;    arm_compute::TensorInfo aclInputToInputWeightsInfo;</div>
+<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;    arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;</div>
+<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;    arm_compute::TensorInfo aclCellToInputWeightsInfo;</div>
+<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;    arm_compute::TensorInfo aclInputGateBiasInfo;</div>
+<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    arm_compute::TensorInfo aclProjectionWeightsInfo;</div>
+<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    arm_compute::TensorInfo aclProjectionBiasInfo;</div>
+<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    arm_compute::TensorInfo aclCellToForgetWeightsInfo;</div>
+<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;    arm_compute::TensorInfo aclCellToOutputWeightsInfo;</div>
+<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160; </div>
+<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    arm_compute::TensorInfo aclInputLayerNormWeightsInfo;</div>
+<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;</div>
+<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;    arm_compute::TensorInfo aclCellLayerNormWeightsInfo;</div>
+<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;    arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;</div>
+<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160; </div>
+<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div>
+<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    {</div>
+<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;        <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>)</div>
+<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;        {</div>
+<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;            aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a36fa9439fda2e72234411956a1c7e64f">GetCellToInputWeights</a>());</div>
+<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;        }</div>
+<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;        aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#afa2b04197a764428a8c3a648de8058fc">GetInputToInputWeights</a>());</div>
+<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;        aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ad159f9edbddeeb6cf6ff0ba042481ba8">GetRecurrentToInputWeights</a>());</div>
+<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;        aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae1d5a487fcd13852927c8a2b9f9dfeb6">GetInputGateBias</a>());</div>
+<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160; </div>
+<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;        lstm_params_info.set_cifg_params(&amp;aclInputToInputWeightsInfo,</div>
+<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;                                         &amp;aclRecurrentToInputWeightsInfo,</div>
+<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;                                         descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> ? &amp;aclCellToInputWeightsInfo : <span class="keyword">nullptr</span>,</div>
+<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;                                         &amp;aclInputGateBiasInfo);</div>
+<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;    }</div>
+<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160; </div>
+<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;    <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div>
+<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;    {</div>
+<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;        <span class="keywordflow">if</span> (paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">m_ProjectionBias</a> != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;        {</div>
+<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;            aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a9f2cce936b4df49c487eaca513bf55ca">GetProjectionBias</a>());</div>
+<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;        }</div>
+<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;        aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a18038725f71bb5c5bd03c02cc164f879">GetProjectionWeights</a>());</div>
+<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160; </div>
+<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;        lstm_params_info.set_projection_params(&amp;aclProjectionWeightsInfo,</div>
+<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;                                               paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">m_ProjectionBias</a> ? &amp;aclProjectionBiasInfo : <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;    }</div>
+<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160; </div>
+<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;    <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>)</div>
+<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;    {</div>
+<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a0e31db1891d11bbe0d8556c01e9812ef">GetCellToForgetWeights</a>());</div>
+<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a35825b1ec5bc2b14c8eac60887dbcf19">GetCellToOutputWeights</a>());</div>
+<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160; </div>
+<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;        lstm_params_info.set_peephole_params(&amp;aclCellToForgetWeightsInfo, &amp;aclCellToOutputWeightsInfo);</div>
+<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;    }</div>
+<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160; </div>
+<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;    <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>)</div>
+<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;    {</div>
+<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;        <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div>
+<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;        {</div>
+<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;            aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a3d2f638ba83ae5dad0094c006220c232">GetInputLayerNormWeights</a>());</div>
+<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;        }</div>
+<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;        aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ab50b4ccb0b84f6427996f76083a4107a">GetForgetLayerNormWeights</a>());</div>
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+<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;        aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a045674b768295e617d7060f96f162366">GetOutputLayerNormWeights</a>());</div>
+<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160; </div>
+<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;        lstm_params_info.set_layer_normalization_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> ? <span class="keyword">nullptr</span> :</div>
+<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;                                                        &amp;aclInputLayerNormWeightsInfo,</div>
+<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;                                                        &amp;aclForgetLayerNormWeightsInfo,</div>
+<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;                                                        &amp;aclCellLayerNormWeightsInfo,</div>
+<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;                                                        &amp;aclOutputLayerNormWeightsInfo);</div>
+<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;    }</div>
+<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160; </div>
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+<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;                                             descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a>,</div>
+<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;                                             descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>,</div>
+<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;                                             descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a>);</div>
+<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160; </div>
+<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;    lstm_params_info.set_hidden_state_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a>, descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>);</div>
+<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160; </div>
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+<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;        <span class="comment">// Set LSTM input and output ITensors depending on:</span></div>
+<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;        <span class="comment">// input format (timeMajor) &amp; number of LSTM batches (maxTime).</span></div>
+<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;        arm_compute::ITensorInfo* outputLSTM;</div>
+<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;        arm_compute::ITensorInfo* inputLSTM;</div>
+<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160; </div>
+<div class="line"><a name="l00706"></a><span class="lineno">  706</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="l00707"></a><span class="lineno">  707</span>&#160;        <span class="comment">// Set input of LSTM to be first input ITensor.</span></div>
+<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;        <span class="comment">// Set output of LSTM to be final output ITensor.</span></div>
+<div class="line"><a name="l00709"></a><span class="lineno">  709</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="l00710"></a><span class="lineno">  710</span>&#160;        <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; !descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;        {</div>
+<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(aclInputInfo.tensor_shape(), 1U);</div>
+<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(aclOutputInfo.tensor_shape(), 1U);</div>
+<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160; </div>
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+<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;            inputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::TensorInfo*<span class="keyword">&gt;</span>(&amp;aclInputInfo);</div>
+<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160; </div>
+<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;            <span class="keyword">const_cast&lt;</span>arm_compute::TensorInfo*<span class="keyword">&gt;</span>(&amp;aclOutputInfo)-&gt;set_tensor_shape(acl_output_shape_shrink);</div>
+<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;            outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::TensorInfo*<span class="keyword">&gt;</span>(&amp;aclOutputInfo);</div>
+<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;        }</div>
+<div class="line"><a name="l00727"></a><span class="lineno">  727</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="l00728"></a><span class="lineno">  728</span>&#160;        <span class="comment">// Set input of LSTM to be output of initial permute.</span></div>
+<div class="line"><a name="l00729"></a><span class="lineno">  729</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="l00730"></a><span class="lineno">  730</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="l00731"></a><span class="lineno">  731</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; !descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
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+<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;            <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
+<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;            aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink);</div>
+<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;            inputLSTM = &amp;aclPermuteOutInfo;</div>
+<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160; </div>
+<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;            outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensorInfo*<span class="keyword">&gt;</span>(concatInputsTensorInfosPtr[i]);</div>
+<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;        }</div>
+<div class="line"><a name="l00741"></a><span class="lineno">  741</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="l00742"></a><span class="lineno">  742</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;        {</div>
+<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;            inputLSTM = splitterOutputsTensorInfosPtr[i];</div>
+<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;            outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensorInfo*<span class="keyword">&gt;</span>(concatInputsTensorInfosPtr[i]);</div>
+<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;        }</div>
+<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160; </div>
+<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;        statusLSTM = arm_compute::NEQLSTMLayer::validate(inputLSTM,</div>
+<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;                                                         &amp;aclInputToForgetWeightsInfo,</div>
+<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;                                                         &amp;aclInputToCellWeightsInfo,</div>
+<div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;                                                         &amp;aclInputToOutputWeightsInfo,</div>
+<div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;                                                         &amp;aclRecurrentToForgetWeightsInfo,</div>
+<div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;                                                         &amp;aclRecurrentToCellWeightsInfo,</div>
+<div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;                                                         &amp;aclRecurrentToOutputWeightsInfo,</div>
+<div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;                                                         &amp;aclForgetGateBiasInfo,</div>
+<div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;                                                         &amp;aclCellBiasInfo,</div>
+<div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;                                                         &amp;aclOutputGateBiasInfo,</div>
+<div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;                                                         &amp;aclCellStateInInfo,</div>
+<div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;                                                         &amp;aclOutputStateInInfo,</div>
+<div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;                                                         &amp;aclCellStateOutInfo,</div>
+<div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;                                                         &amp;aclOutputStateOutInfo,</div>
+<div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;                                                         outputLSTM,</div>
+<div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;                                                         lstm_params_info);</div>
+<div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    }</div>
+<div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160; </div>
+<div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    <span class="comment">// Concat validate</span></div>
+<div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160; </div>
+<div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;    <span class="comment">// Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.</span></div>
+<div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(concatInputsTensorInfosPtr[0]-&gt;tensor_shape(), 1U);</div>
+<div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandTimeMajor({1, shape[0], shape[1]});</div>
+<div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandBatchMajor({shape[0], 1, shape[1]});</div>
+<div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160; </div>
+<div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> concatOutputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(output);</div>
+<div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    concatOutputTensorInfo.SetShape(timeMajorShapeOutput);</div>
+<div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;    arm_compute::TensorInfo aclConcatOutputTensorInfo= BuildArmComputeTensorInfo(concatOutputTensorInfo);</div>
+<div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160; </div>
+<div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;    <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL concat does not work with only one element to concatenate.</span></div>
+<div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;    {</div>
+<div class="line"><a name="l00781"></a><span class="lineno">  781</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="l00782"></a><span class="lineno">  782</span>&#160;        {</div>
+<div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;            <span class="keyword">auto</span> acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor);</div>
+<div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;            concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand);</div>
+<div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;        }</div>
+<div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160; </div>
+<div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisConcat = CalcAclAxis(numberDimensions, dimension);</div>
+<div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;        <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;        {</div>
+<div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;            statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr,</div>
+<div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;                                                                     &amp;aclConcatOutputTensorInfo,</div>
+<div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;                                                                     aclAxisConcat);</div>
+<div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;        }</div>
+<div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;        {</div>
+<div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;            statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr,</div>
+<div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;                                                                     &amp;aclOutputInfo,</div>
+<div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;                                                                     aclAxisConcat);</div>
+<div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;        }</div>
+<div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;    }</div>
+<div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;    <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div>
+<div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;    <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div>
+<div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;    <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;    {</div>
+<div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;        <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;        {</div>
+<div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;            <span class="keyword">const_cast&lt;</span>arm_compute::TensorInfo*<span class="keyword">&gt;</span>(&amp;aclInputInfo)-&gt;set_tensor_shape(</div>
+<div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;                BuildArmComputeTensorShape(shapeExpandBatchMajor));</div>
+<div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;        }</div>
+<div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;        {</div>
+<div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;            <span class="keyword">const_cast&lt;</span>arm_compute::TensorInfo*<span class="keyword">&gt;</span>(&amp;aclInputInfo)-&gt;set_tensor_shape(</div>
+<div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160;                BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
+<div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;        }</div>
+<div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;    }</div>
+<div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160; </div>
+<div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;    <span class="comment">// Permute validate</span></div>
+<div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;    <span class="comment">//</span></div>
+<div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;    <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div>
+<div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    {</div>
+<div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;        <span class="comment">// Output now time major. Permute output back to batch major.</span></div>
+<div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;        <span class="keywordflow">if</span> (maxTime != 1)</div>
+<div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;        {</div>
+<div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;            statusPermute2 = arm_compute::NEPermute::validate(&amp;aclConcatOutputTensorInfo,</div>
+<div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;                                                              &amp;aclOutputInfo,</div>
+<div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;                                                              arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;        }</div>
+<div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;        {</div>
+<div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;            statusPermute2 = arm_compute::NEPermute::validate(concatInputsTensorInfosPtr[0],</div>
+<div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;                                                              &amp;aclOutputInfo,</div>
+<div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;                                                              arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;        }</div>
+<div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;    }</div>
+<div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160; </div>
+<div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;    <span class="keyword">auto</span> okCode = arm_compute::ErrorCode::OK;</div>
+<div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    <span class="keywordflow">if</span> (statusPermute1.error_code() == okCode &amp;&amp;</div>
+<div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;        statusSplit.error_code()    == okCode &amp;&amp;</div>
+<div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;        statusLSTM .error_code()    == okCode &amp;&amp;</div>
+<div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160;        statusConcat.error_code()   == okCode &amp;&amp;</div>
+<div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;        statusPermute2.error_code() == okCode)</div>
+<div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;    {</div>
+<div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div>
+<div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;                                   <span class="stringliteral">&quot;All Unidirectional Sequence LSTM layer validate status OK.&quot;</span>);</div>
+<div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;    }</div>
+<div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;    <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160;    {</div>
+<div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::RUNTIME_ERROR,</div>
+<div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;                                   <span class="stringliteral">&quot;Unidirectional Sequence LSTM layer validate status failed.&quot;</span>);</div>
+<div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;    }</div>
+<div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;}</div>
+<div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160; </div>
+<div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;<span class="keywordtype">void</span> NeonUnidirectionalSequenceLstmWorkload::FreeUnusedTensors()</div>
+<div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;{</div>
+<div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;    FreeTensorIfUnused(m_InputToInputWeightsTensor);</div>
+<div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;    FreeTensorIfUnused(m_InputToForgetWeightsTensor);</div>
+<div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160;    FreeTensorIfUnused(m_InputToCellWeightsTensor);</div>
+<div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;    FreeTensorIfUnused(m_InputToOutputWeightsTensor);</div>
+<div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);</div>
+<div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;    FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);</div>
+<div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;    FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);</div>
+<div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;    FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);</div>
+<div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;    FreeTensorIfUnused(m_CellToInputWeightsTensor);</div>
+<div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;    FreeTensorIfUnused(m_CellToForgetWeightsTensor);</div>
+<div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;    FreeTensorIfUnused(m_CellToOutputWeightsTensor);</div>
+<div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;    FreeTensorIfUnused(m_InputGateBiasTensor);</div>
+<div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;    FreeTensorIfUnused(m_ForgetGateBiasTensor);</div>
+<div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;    FreeTensorIfUnused(m_CellBiasTensor);</div>
+<div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;    FreeTensorIfUnused(m_OutputGateBiasTensor);</div>
+<div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;    FreeTensorIfUnused(m_ProjectionWeightsTensor);</div>
+<div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;    FreeTensorIfUnused(m_ProjectionBiasTensor);</div>
+<div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;    FreeTensorIfUnused(m_InputLayerNormWeightsTensor);</div>
+<div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;    FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);</div>
+<div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;    FreeTensorIfUnused(m_CellLayerNormWeightsTensor);</div>
+<div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;    FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);</div>
+<div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;}</div>
+<div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160; </div>
+<div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;} <span class="comment">//namespace armnn</span></div>
+</div><!-- fragment --></div><!-- contents -->
+</div><!-- doc-content -->
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a3b3c26330a05bf4ea40f8a6b402be354"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a3b3c26330a05bf4ea40f8a6b402be354">armnn::LstmInputParamsInfo::GetInputToCellWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetInputToCellWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00129">LstmParams.hpp:129</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a35825b1ec5bc2b14c8eac60887dbcf19"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a35825b1ec5bc2b14c8eac60887dbcf19">armnn::LstmInputParamsInfo::GetCellToOutputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetCellToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00161">LstmParams.hpp:161</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a3dcd10ca3ea2e132558b1e2814668c15"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#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.xhtml#l01121">Descriptors.hpp:1121</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml">armnn::LstmInputParamsInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00063">LstmParams.hpp:63</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a045674b768295e617d7060f96f162366"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a045674b768295e617d7060f96f162366">armnn::LstmInputParamsInfo::GetOutputLayerNormWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetOutputLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00201">LstmParams.hpp:201</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_origins_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::OriginsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00192">Descriptors.cpp:192</a></div></div>
+<div class="ttc" id="anamespacearmnn_utils_xhtml_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.xhtml#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.xhtml#l00019">TensorUtils.cpp:19</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_afa2b04197a764428a8c3a648de8058fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#afa2b04197a764428a8c3a648de8058fc">armnn::LstmInputParamsInfo::GetInputToInputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetInputToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00121">LstmParams.hpp:121</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_neon_unidirectional_sequence_lstm_workload_xhtml_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">armnn::NeonUnidirectionalSequenceLstmWorkload::Execute</a></div><div class="ttdeci">virtual void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.xhtml#l00465">NeonUnidirectionalSequenceLstmWorkload.cpp:465</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01069">Descriptors.hpp:1069</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ae22fc962c59e7c24986718f5af0020db"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">armnn::LstmInputParamsInfo::m_ProjectionBias</a></div><div class="ttdeci">const TensorInfo * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00105">LstmParams.hpp:105</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ac81393ef433b0c7c337f9f0d55f41ae4"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ac81393ef433b0c7c337f9f0d55f41ae4">armnn::LstmInputParamsInfo::GetForgetGateBias</a></div><div class="ttdeci">const TensorInfo &amp; GetForgetGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00169">LstmParams.hpp:169</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_origins_descriptor_xhtml_a5b192c5fcd96a0f75542524cf646b355"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">armnn::OriginsDescriptor::SetConcatAxis</a></div><div class="ttdeci">void SetConcatAxis(unsigned int concatAxis)</div><div class="ttdoc">Set the concatenation axis value.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00158">Descriptors.cpp:158</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#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.xhtml#l01113">Descriptors.hpp:1113</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#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.xhtml#l01119">Descriptors.hpp:1119</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a534af7e4f3a6d50a6dab05abc245133d"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a534af7e4f3a6d50a6dab05abc245133d">armnn::LstmInputParamsInfo::GetRecurrentToForgetWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetRecurrentToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00141">LstmParams.hpp:141</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00244">Descriptors.hpp:244</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_origins_descriptor_xhtml_a379929e3b277f1ef94f3ce645870589d"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a379929e3b277f1ef94f3ce645870589d">armnn::OriginsDescriptor::GetConcatAxis</a></div><div class="ttdeci">unsigned int GetConcatAxis() const</div><div class="ttdoc">Get the concatenation axis value.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00162">Descriptors.cpp:162</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a18038725f71bb5c5bd03c02cc164f879"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a18038725f71bb5c5bd03c02cc164f879">armnn::LstmInputParamsInfo::GetProjectionWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetProjectionWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00181">LstmParams.hpp:181</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ae1d5a487fcd13852927c8a2b9f9dfeb6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae1d5a487fcd13852927c8a2b9f9dfeb6">armnn::LstmInputParamsInfo::GetInputGateBias</a></div><div class="ttdeci">const TensorInfo &amp; GetInputGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00165">LstmParams.hpp:165</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a7dac08f19a1b235d5256d39136848a09"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a7dac08f19a1b235d5256d39136848a09">armnn::LstmInputParamsInfo::GetInputToForgetWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetInputToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00125">LstmParams.hpp:125</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">armnn::LstmDescriptor::m_InputIntermediateScale</a></div><div class="ttdeci">float m_InputIntermediateScale</div><div class="ttdoc">Input intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_views_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::ViewsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00300">Descriptors.cpp:300</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a9f2cce936b4df49c487eaca513bf55ca"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a9f2cce936b4df49c487eaca513bf55ca">armnn::LstmInputParamsInfo::GetProjectionBias</a></div><div class="ttdeci">const TensorInfo &amp; GetProjectionBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00185">LstmParams.hpp:185</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">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.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">@Brief Set the view origin coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00310">Descriptors.cpp:310</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml_a611208865d55ea576cc89ac86d7c19b7"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00060">NeonWorkloadUtils.hpp:60</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml_a8cbabc875597b3bed0ccdc0adb289fde"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00244">ArmComputeUtils.hpp:244</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00691">WorkloadData.hpp:691</a></div></div>
+<div class="ttc" id="a_neon_unidirectional_sequence_lstm_workload_8hpp_xhtml"><div class="ttname"><a href="_neon_unidirectional_sequence_lstm_workload_8hpp.xhtml">NeonUnidirectionalSequenceLstmWorkload.hpp</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">armnn::LstmDescriptor::m_CellIntermediateScale</a></div><div class="ttdeci">float m_CellIntermediateScale</div><div class="ttdoc">Cell intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01127">Descriptors.hpp:1127</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a3d2f638ba83ae5dad0094c006220c232"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a3d2f638ba83ae5dad0094c006220c232">armnn::LstmInputParamsInfo::GetInputLayerNormWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetInputLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00189">LstmParams.hpp:189</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ad159f9edbddeeb6cf6ff0ba042481ba8"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ad159f9edbddeeb6cf6ff0ba042481ba8">armnn::LstmInputParamsInfo::GetRecurrentToInputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetRecurrentToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00137">LstmParams.hpp:137</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
+<div class="ttc" id="a_neon_workload_utils_8hpp_xhtml_a9165e41bcaf1b90f9ff91ef681e88c4f"><div class="ttname"><a href="_neon_workload_utils_8hpp.xhtml#a9165e41bcaf1b90f9ff91ef681e88c4f">ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID(name, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.xhtml#l00024">NeonWorkloadUtils.hpp:24</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ae5bfdd423b16f990c1713ef9f91f947b"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae5bfdd423b16f990c1713ef9f91f947b">armnn::LstmInputParamsInfo::GetRecurrentToCellWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetRecurrentToCellWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00145">LstmParams.hpp:145</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">armnn::LstmDescriptor::m_HiddenStateScale</a></div><div class="ttdeci">float m_HiddenStateScale</div><div class="ttdoc">Hidden State quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01133">Descriptors.hpp:1133</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">armnn::LstmDescriptor::m_HiddenStateZeroPoint</a></div><div class="ttdeci">int32_t m_HiddenStateZeroPoint</div><div class="ttdoc">Hidden State zero point.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01131">Descriptors.hpp:1131</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ae0da94ba17ce67b95b5b9d6e5adc4271"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae0da94ba17ce67b95b5b9d6e5adc4271">armnn::LstmInputParamsInfo::GetOutputGateBias</a></div><div class="ttdeci">const TensorInfo &amp; GetOutputGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00177">LstmParams.hpp:177</a></div></div>
+<div class="ttc" id="a_arm_compute_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.xhtml">ArmComputeTensorUtils.hpp</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#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.xhtml#l01115">Descriptors.hpp:1115</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ad5f4be37766b41f342dd196cb1c6e141"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ad5f4be37766b41f342dd196cb1c6e141">armnn::LstmInputParamsInfo::GetCellBias</a></div><div class="ttdeci">const TensorInfo &amp; GetCellBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00173">LstmParams.hpp:173</a></div></div>
+<div class="ttc" id="a_arm_compute_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_utils_8hpp.xhtml">ArmComputeUtils.hpp</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_i_acl_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_acl_tensor_handle.xhtml">armnn::IAclTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_tensor_handle_8hpp_source.xhtml#l00016">ArmComputeTensorHandle.hpp:16</a></div></div>
+<div class="ttc" id="a_neon_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.hpp</a></div></div>
+<div class="ttc" id="a_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml_a42561b8004ee341ac089d5f1657120db"><div class="ttname"><a href="namespacearmnn.xhtml#a42561b8004ee341ac089d5f1657120db">armnn::NeonUnidirectionalSequenceLstmWorkloadValidate</a></div><div class="ttdeci">arm_compute::Status NeonUnidirectionalSequenceLstmWorkloadValidate(const TensorInfo &amp;input, const TensorInfo &amp;outputStateIn, const TensorInfo &amp;cellStateIn, const TensorInfo &amp;outputStateOut, const TensorInfo &amp;cellStateOut, const TensorInfo &amp;output, const UnidirectionalSequenceLstmDescriptor &amp;descriptor, const LstmInputParamsInfo &amp;paramsInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.xhtml#l00491">NeonUnidirectionalSequenceLstmWorkload.cpp:491</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70b"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a></div><div class="ttdeci">Status</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00042">Types.hpp:42</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">armnn::LstmDescriptor::m_OutputIntermediateScale</a></div><div class="ttdeci">float m_OutputIntermediateScale</div><div class="ttdoc">Output intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01129">Descriptors.hpp:1129</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">armnn::LstmDescriptor::m_ForgetIntermediateScale</a></div><div class="ttdeci">float m_ForgetIntermediateScale</div><div class="ttdoc">Forget intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01125">Descriptors.hpp:1125</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_aaf1af3bc828c5daa4a5c0bac28f63cc3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#aaf1af3bc828c5daa4a5c0bac28f63cc3">armnn::LstmInputParamsInfo::GetCellLayerNormWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetCellLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00197">LstmParams.hpp:197</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer.</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_neon_unidirectional_sequence_lstm_workload_xhtml_a39a30590f33a409c3c39181d446621aa"><div class="ttname"><a href="classarmnn_1_1_neon_unidirectional_sequence_lstm_workload.xhtml#a39a30590f33a409c3c39181d446621aa">armnn::NeonUnidirectionalSequenceLstmWorkload::NeonUnidirectionalSequenceLstmWorkload</a></div><div class="ttdeci">NeonUnidirectionalSequenceLstmWorkload(const UnidirectionalSequenceLstmQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_unidirectional_sequence_lstm_workload_8cpp_source.xhtml#l00033">NeonUnidirectionalSequenceLstmWorkload.cpp:33</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a36fa9439fda2e72234411956a1c7e64f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a36fa9439fda2e72234411956a1c7e64f">armnn::LstmInputParamsInfo::GetCellToInputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetCellToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00153">LstmParams.hpp:153</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01109">Descriptors.hpp:1109</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a0e31db1891d11bbe0d8556c01e9812ef"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a0e31db1891d11bbe0d8556c01e9812ef">armnn::LstmInputParamsInfo::GetCellToForgetWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetCellToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00157">LstmParams.hpp:157</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00201">Descriptors.hpp:201</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#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.xhtml#l00066">WorkloadData.hpp:66</a></div></div>
+<div class="ttc" id="a_profiling_8hpp_xhtml_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.xhtml#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.xhtml#l00227">Profiling.hpp:227</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#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.xhtml#l01117">Descriptors.hpp:1117</a></div></div>
+<div class="ttc" id="a_neon_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_neon_tensor_handle_8hpp.xhtml">NeonTensorHandle.hpp</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_afe4d25acd31b98dee6f6b28d4d756071"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#afe4d25acd31b98dee6f6b28d4d756071">armnn::LstmInputParamsInfo::GetRecurrentToOutputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetRecurrentToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00149">LstmParams.hpp:149</a></div></div>
+<div class="ttc" id="a_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01111">Descriptors.hpp:1111</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#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.xhtml#l00193">Tensor.hpp:193</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_ab50b4ccb0b84f6427996f76083a4107a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ab50b4ccb0b84f6427996f76083a4107a">armnn::LstmInputParamsInfo::GetForgetLayerNormWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetForgetLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00193">LstmParams.hpp:193</a></div></div>
+<div class="ttc" id="a_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_utils_8hpp.xhtml">WorkloadUtils.hpp</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_neon_base_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_neon_base_workload.xhtml">armnn::NeonBaseWorkload&lt; UnidirectionalSequenceLstmQueueDescriptor &gt;</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_lstm_input_params_info_xhtml_a800adf0f61e84d706060f63037c1a336"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a800adf0f61e84d706060f63037c1a336">armnn::LstmInputParamsInfo::GetInputToOutputWeights</a></div><div class="ttdeci">const TensorInfo &amp; GetInputToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00133">LstmParams.hpp:133</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_origins_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::OriginsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">@Brief Set the view origin coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00167">Descriptors.cpp:167</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
+<div class="ttc" id="anamespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div>
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+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_d86eb514662c7c08e168285f21d00ea1.xhtml">neon</a></li><li class="navelem"><a class="el" href="dir_369c3c20501d0d10bd0354bf11c2f559.xhtml">workloads</a></li><li class="navelem"><a class="el" href="_neon_unidirectional_sequence_lstm_workload_8cpp.xhtml">NeonUnidirectionalSequenceLstmWorkload.cpp</a></li>
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