IVGCVSW-7968 Update Doxygen docu for 24.02
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I8c1e45815c6cf78f80d6f2c0959a5bbba6cd11de
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+<div class="header">
+ <div class="summary">
+<a href="#pub-methods">Public Member Functions</a> |
+<a href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload-members.html">List of all members</a> </div>
+ <div class="headertitle">
+<div class="title">NeonUnidirectionalSequenceLstmFloatWorkload Class Reference</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p><code>#include <<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a>></code></p>
+<div class="dynheader">
+Inheritance diagram for NeonUnidirectionalSequenceLstmFloatWorkload:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload__inherit__graph.svg" width="266" height="291"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+<center><span class="legend">[<a target="top" href="graph_legend.html">legend</a>]</span></center></div>
+<div class="dynheader">
+Collaboration diagram for NeonUnidirectionalSequenceLstmFloatWorkload:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload__coll__graph.svg" width="271" height="308"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+<center><span class="legend">[<a target="top" href="graph_legend.html">legend</a>]</span></center></div>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
+Public Member Functions</h2></td></tr>
+<tr class="memitem:acceeccc54cc2871ec72da81e48e7ef1c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html#acceeccc54cc2871ec72da81e48e7ef1c">NeonUnidirectionalSequenceLstmFloatWorkload</a> (const <a class="el" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">UnidirectionalSequenceLstmQueueDescriptor</a> &descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &info)</td></tr>
+<tr class="separator:acceeccc54cc2871ec72da81e48e7ef1c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae071e8822437c78baea75c3aef3a263a"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a> () const override</td></tr>
+<tr class="separator:ae071e8822437c78baea75c3aef3a263a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="inherit_header pub_methods_classarmnn_1_1_typed_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_typed_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_typed_workload.html">TypedWorkload< QueueDescriptor, DataTypes ></a></td></tr>
+<tr class="memitem:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_typed_workload.html#aa617fec9998f9650150a758b68498865">TypedWorkload</a> (const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> &descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &info)</td></tr>
+<tr class="separator:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="inherit_header pub_methods_classarmnn_1_1_base_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_base_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_base_workload.html">BaseWorkload< QueueDescriptor ></a></td></tr>
+<tr class="memitem:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#af2ef420610280dc5a661cd3d4836d5a2">BaseWorkload</a> (const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> &descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &info)</td></tr>
+<tr class="separator:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a163c04b26f9804eafc598a047128f887 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">virtual const std::string & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a163c04b26f9804eafc598a047128f887">GetName</a> () const override</td></tr>
+<tr class="separator:a163c04b26f9804eafc598a047128f887 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae1c43d025fc90382d7aff7a500937e2c inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#ae1c43d025fc90382d7aff7a500937e2c">ExecuteAsync</a> (<a class="el" href="structarmnn_1_1experimental_1_1_execution_data.html">ExecutionData</a> &executionData) override</td></tr>
+<tr class="separator:ae1c43d025fc90382d7aff7a500937e2c inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a81627f96ba06d76e147f7d392a8117ed">PostAllocationConfigure</a> () override</td></tr>
+<tr class="separator:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a965cf380c7adf547d0f14b3f6d1da249">GetData</a> () const</td></tr>
+<tr class="separator:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aaff95a48875d8fb4a616352906660ca9 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">arm::pipe::ProfilingGuid </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">GetGuid</a> () const final</td></tr>
+<tr class="separator:aaff95a48875d8fb4a616352906660ca9 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0c326c344355d8423217e9431781f2ee inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a0c326c344355d8423217e9431781f2ee">SupportsTensorHandleReplacement</a> () const override</td></tr>
+<tr class="separator:a0c326c344355d8423217e9431781f2ee inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab0a67f8179ddb997dda0070a6661f837 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#ab0a67f8179ddb997dda0070a6661f837">ReplaceInputTensorHandle</a> (<a class="el" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a> *tensorHandle, unsigned int slot) override</td></tr>
+<tr class="separator:ab0a67f8179ddb997dda0070a6661f837 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acc08590544f05c641d21c724aedf26dd inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#acc08590544f05c641d21c724aedf26dd">ReplaceOutputTensorHandle</a> (<a class="el" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a> *tensorHandle, unsigned int slot) override</td></tr>
+<tr class="separator:acc08590544f05c641d21c724aedf26dd inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="inherit_header pub_methods_classarmnn_1_1_i_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_i_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_i_workload.html">IWorkload</a></td></tr>
+<tr class="memitem:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#a69c83c02ae8de866bc7a46c49e69c1ba">~IWorkload</a> ()</td></tr>
+<tr class="separator:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#ab81312bd5e64cbae2803de9f243bdb32">RegisterDebugCallback</a> (const <a class="el" href="namespacearmnn.html#a15f3ad9b5e4e3d46b0a6dda246a7bc28">DebugCallbackFunction</a> &)</td></tr>
+<tr class="separator:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2d2834d1029217934f504e3e59579081 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classarmnn_1_1_optional.html">armnn::Optional</a>< <a class="el" href="structarmnn_1_1_memory_requirements.html">armnn::MemoryRequirements</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#a2d2834d1029217934f504e3e59579081">GetMemoryRequirements</a> ()</td></tr>
+<tr class="separator:a2d2834d1029217934f504e3e59579081 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2"> </td></tr>
+</table><table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
+Additional Inherited Members</h2></td></tr>
+<tr class="inherit_header pro_attribs_classarmnn_1_1_base_workload"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classarmnn_1_1_base_workload')"><img src="closed.png" alt="-"/> Protected Attributes inherited from <a class="el" href="classarmnn_1_1_base_workload.html">BaseWorkload< QueueDescriptor ></a></td></tr>
+<tr class="memitem:afb8d2c8817c75de9d01a4c0e0d5c160b inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a></td></tr>
+<tr class="separator:afb8d2c8817c75de9d01a4c0e0d5c160b inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4c2b3ca86eec6c199364671af267cd2c inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const arm::pipe::ProfilingGuid </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a4c2b3ca86eec6c199364671af267cd2c">m_Guid</a></td></tr>
+<tr class="separator:a4c2b3ca86eec6c199364671af267cd2c inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a77806f89d6edb879d3f6c6b6b18168a7 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a77806f89d6edb879d3f6c6b6b18168a7">m_Name</a></td></tr>
+<tr class="separator:a77806f89d6edb879d3f6c6b6b18168a7 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock">
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html#l00021">21</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a>.</p>
+</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
+<a id="acceeccc54cc2871ec72da81e48e7ef1c"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#acceeccc54cc2871ec72da81e48e7ef1c">◆ </a></span>NeonUnidirectionalSequenceLstmFloatWorkload()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html">NeonUnidirectionalSequenceLstmFloatWorkload</a> </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">UnidirectionalSequenceLstmQueueDescriptor</a> & </td>
+ <td class="paramname"><em>descriptor</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> & </td>
+ <td class="paramname"><em>info</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html#l00032">32</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  : FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)</div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> {</div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// Report Profiling Details</span></div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="_profiling_8hpp.html#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">"NeonUnidirectionalSequenceLstmFloatWorkload_Construct"</span>,</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  descriptor.m_Parameters,</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  info,</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  </div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> arm_compute::ITensor& input = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetTensor();</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  arm_compute::ITensor& output = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])->GetTensor();</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  </div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  TensorInfo inputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_InputTensorInfos[0];</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  TensorInfo outputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_OutputTensorInfos[0];</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  </div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> armComputeDataType = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetDataType();</div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> armnnDataType = GetArmNNDataType(armComputeDataType);</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  TensorShape inputLayerShape = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetShape();</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  TensorShape cellStateLayerShape = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])->GetShape();</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  TensorShape outputLayerShape = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])->GetShape();</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  </div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputLayerShape[2];</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = cellStateLayerShape[1];</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  </div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">const</span> TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  </div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.</span></div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  std::unique_ptr<arm_compute::NEPermute> layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  </div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  TensorInfo permuteOutInfo = inputInfo;</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  permuteOutInfo.SetShape(timeMajorShapeInput);</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);</div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  </div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Permute to time major format.</span></div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  layer->configure(&input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  m_Permute1.reset(layer.release());</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  </div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Split and Concat Tensors</span></div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <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="l00084"></a><span class="lineno"> 84</span>  {</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  arm_compute::Tensor splitter_out;</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  arm_compute::Tensor concat_in;</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  </div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">auto</span> splitterTensorInfo = inputInfo;</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">auto</span> concatTensorInfo = outputInfo;</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  splitterTensorInfo.SetShape({batchSize, inputSize});</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  concatTensorInfo.SetShape({batchSize, outputSize});</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  BuildArmComputeTensor(splitter_out, splitterTensorInfo);</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  BuildArmComputeTensor(concat_in, concatTensorInfo);</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  </div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  </div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// append to std::vector<arm_compute::Tensor></span></div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  m_SplitterOutputsTensors.push_back(std::move(splitter_out));</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  m_ConcatInputsTensors.push_back(std::move(concat_in));</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  }</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  </div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <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="l00104"></a><span class="lineno"> 104</span>  {</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// append to std::vector<arm_compute::ITensor*></span></div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]);</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]);</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  </div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// Split</span></div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <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="l00115"></a><span class="lineno"> 115</span>  </div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <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="l00117"></a><span class="lineno"> 117</span>  {</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  ViewsDescriptor splitterDesc(maxTime, numberDimensions);</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[3] = {1, batchSize, inputSize};</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIdx = 0u; outputIdx < maxTime; ++outputIdx)</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx)</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  {</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  }</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::set<unsigned int> splitAxis = <a class="code" href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">ComputeSplitAxis</a>(splitterDesc, timeMajorShapeInput);</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  </div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::unique_ptr<arm_compute::NESplit> split_layer(<span class="keyword">new</span> arm_compute::NESplit());</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(),</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  *splitAxis.begin());</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  } <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit);</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  </div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  split_layer->prepare();</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  m_Splitter.reset(split_layer.release());</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  </div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// Lstm</span></div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  </div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  </div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  </div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  </div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  </div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  </div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  </div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias->GetTensorInfo());</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  </div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias->GetTensorInfo());</div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  </div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias->GetTensorInfo());</div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  </div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  {</div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  </div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  </div>
+<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  </div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias->GetTensorInfo());</div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  </div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  m_RecurrentToInputWeightsTensor.get(),</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  m_InputGateBiasTensor.get());</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  }</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  </div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  {</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  </div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias->GetTensorInfo());</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  }</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  </div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  </div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  {</div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  </div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  </div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  </div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div>
+<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  </div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  </div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  </div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights->GetTensorInfo());</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  </div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">auto</span> inputNormWeightTensor = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  lstm_param.set_layer_normalization_params(inputNormWeightTensor,</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  m_ForgetLayerNormWeightsTensor.get(),</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  m_CellLayerNormWeightsTensor.get(),</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  m_OutputLayerNormWeightsTensor.get());</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  </div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  arm_compute::ITensor& output_state_in = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])->GetTensor();</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  arm_compute::ITensor& cell_state_in = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])->GetTensor();</div>
+<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  </div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  arm_compute::ITensor& output_state_out = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])->GetTensor();</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  arm_compute::ITensor& cell_state_out = <span class="keyword">static_cast<</span>IAclTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])->GetTensor();</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  </div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  m_ScratchBuffer = std::make_unique<arm_compute::Tensor>();</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  {</div>
+<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">// scratch_buffer [num_units * 3, batch_size] with CIFG</span></div>
+<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType));</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  }</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType));</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  </div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">// Need to be set at negative threshold to be compatible for ACL</span></div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresCell;</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresProj;</div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  </div>
+<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="comment">// For preparing the object for the class ActivationLayerInfo, consider 5 situations</span></div>
+<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  arm_compute::ActivationLayerInfo activationLayerInfo =</div>
+<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="namespacearmnn.html#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ActivationFunc);</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  </div>
+<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <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="l00279"></a><span class="lineno"> 279</span>  {</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="comment">// Set LSTM input and output ITensors depending on:</span></div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="comment">// input format (timeMajor) & number of LSTM batches (maxTime).</span></div>
+<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  arm_compute::ITensor* outputLSTM;</div>
+<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  arm_compute::ITensor* inputLSTM;</div>
+<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  </div>
+<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <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="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// Set input of LSTM to be first input ITensor.</span></div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// Set output of LSTM to be final output ITensor.</span></div>
+<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">if</span> (maxTime == 1 && <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(input.info()->tensor_shape(), 1U);</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  TensorShape outputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>((&output)-><a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->tensor_shape(), 1U);</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  </div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  TensorShape outputShapeShrink({outputShape[1], outputShape[2]});</div>
+<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  </div>
+<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">auto</span> acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);</div>
+<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  </div>
+<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  input.info()->set_tensor_shape(acl_input_shape_shrink);</div>
+<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  inputLSTM = <span class="keyword">const_cast<</span>arm_compute::ITensor*<span class="keyword">></span>(&input);</div>
+<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  </div>
+<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  output.info()->set_tensor_shape(acl_output_shape_shrink);</div>
+<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  outputLSTM = &output;</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  }</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <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="l00307"></a><span class="lineno"> 307</span>  <span class="comment">// Set input of LSTM to be output of initial permute.</span></div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">// Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.</span></div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="comment">// LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 && !<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_PermuteFirstOut.info()->tensor_shape(), 1U);</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink);</div>
+<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  inputLSTM = &m_PermuteFirstOut;</div>
+<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  </div>
+<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ITensor*<span class="keyword">></span>(m_ConcatInputs[i]);</div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <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="l00321"></a><span class="lineno"> 321</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div>
+<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  inputLSTM = m_SplitterOutputs[i];</div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ITensor*<span class="keyword">></span>(m_ConcatInputs[i]);</div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  }</div>
+<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  </div>
+<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  std::unique_ptr<arm_compute::NELSTMLayer> lstm_layer(<span class="keyword">new</span> arm_compute::NELSTMLayer());</div>
+<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  lstm_layer->configure(inputLSTM,</div>
+<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  m_InputToForgetWeightsTensor.get(),</div>
+<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  m_InputToCellWeightsTensor.get(),</div>
+<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  m_InputToOutputWeightsTensor.get(),</div>
+<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  m_RecurrentToForgetWeightsTensor.get(),</div>
+<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  m_RecurrentToCellWeightsTensor.get(),</div>
+<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  m_RecurrentToOutputWeightsTensor.get(),</div>
+<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  m_ForgetGateBiasTensor.get(),</div>
+<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  m_CellBiasTensor.get(),</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  m_OutputGateBiasTensor.get(),</div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  &output_state_in,</div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  &cell_state_in,</div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  m_ScratchBuffer.get(),</div>
+<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  &output_state_out,</div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  &cell_state_out,</div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  outputLSTM,</div>
+<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  lstm_param,</div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  activationLayerInfo,</div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  cell_threshold,</div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  projection_threshold);</div>
+<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  </div>
+<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  m_Layers.emplace_back(std::move(lstm_layer));</div>
+<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  }</div>
+<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  </div>
+<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div>
+<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  </div>
+<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights);</div>
+<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights);</div>
+<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights);</div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights);</div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights);</div>
+<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights);</div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias);</div>
+<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias);</div>
+<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias);</div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  </div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  {</div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights);</div>
+<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights);</div>
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div>
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights);</div>
+<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias);</div>
+<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div>
+<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  </div>
+<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div>
+<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  {</div>
+<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights);</div>
+<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias);</div>
+<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  }</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  }</div>
+<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  </div>
+<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div>
+<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  {</div>
+<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights);</div>
+<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights);</div>
+<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  }</div>
+<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  </div>
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div>
+<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  {</div>
+<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
+<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div>
+<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights);</div>
+<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  }</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights);</div>
+<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights);</div>
+<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights);</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  }</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  </div>
+<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// Force Compute Library to perform the necessary copying and reshaping.</span></div>
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="comment">// After which delete all the input tensors that will no longer be needed.</span></div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < m_Layers.size(); ++i)</div>
+<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  {</div>
+<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  m_Layers[i]->prepare();</div>
+<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  }</div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  </div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Concat</span></div>
+<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  </div>
+<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="comment">// Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.</span></div>
+<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  TensorShape shape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_ConcatInputs[0]-><a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->tensor_shape(), 1U);</div>
+<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});</div>
+<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});</div>
+<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  </div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <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="l00418"></a><span class="lineno"> 418</span>  {</div>
+<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <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="l00420"></a><span class="lineno"> 420</span>  {</div>
+<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
+<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  }</div>
+<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  </div>
+<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="namespacearmnn.html#a7863c179ff92feec660c48ab7b95ae55">ConcatDescriptor</a> concatDescriptor(maxTime, numberDimensions); <span class="comment">// maxTime = num inputs (aka. number of views).</span></div>
+<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIdx = 0u; inputIdx < maxTime; ++inputIdx)</div>
+<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  {</div>
+<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx);</div>
+<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  concatDescriptor.SetConcatAxis(dimension);</div>
+<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  }</div>
+<div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  </div>
+<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  m_Concat.reset(<span class="keyword">new</span> arm_compute::NEConcatenateLayer());</div>
+<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis());</div>
+<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  {</div>
+<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  TensorInfo concatOutputTensorInfo = outputInfo;</div>
+<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  concatOutputTensorInfo.SetShape(timeMajorShapeOutput);</div>
+<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  BuildArmComputeTensor(concat_out, concatOutputTensorInfo);</div>
+<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);</div>
+<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  </div>
+<div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);</div>
+<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  }</div>
+<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  {</div>
+<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat);</div>
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  }</div>
+<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  </div>
+<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  m_Concat->prepare();</div>
+<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div>
+<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div>
+<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div>
+<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div>
+<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  {</div>
+<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));</div>
+<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  }</div>
+<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  {</div>
+<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
+<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div>
+<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div>
+<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  </div>
+<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// Permute: only done if input/output are in batch major format.</span></div>
+<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">//</span></div>
+<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
+<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div>
+<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// Output now time major. Permute output back to batch major.</span></div>
+<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::unique_ptr<arm_compute::NEPermute> layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
+<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">if</span> (maxTime != 1)</div>
+<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  {</div>
+<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  layer->configure(&concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div>
+<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div>
+<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  layer->configure(m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
+<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div>
+<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  m_Permute2.reset(layer.release());</div>
+<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  }</div>
+<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  </div>
+<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  FreeUnusedTensors();</div>
+<div class="line"><a name="l00482"></a><span class="lineno"> 482</span> }</div>
+</div><!-- fragment -->
+<p class="reference">References <a class="el" href="_profiling_8hpp_source.html#l00227">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>, <a class="el" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, and <a class="el" href="_workload_data_8hpp_source.html#l00066">QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters</a>.</p>
+
+</div>
+</div>
+<h2 class="groupheader">Member Function Documentation</h2>
+<a id="ae071e8822437c78baea75c3aef3a263a"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae071e8822437c78baea75c3aef3a263a">◆ </a></span>Execute()</h2>
+
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">void Execute </td>
+ <td>(</td>
+ <td class="paramname"></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Implements <a class="el" href="classarmnn_1_1_i_workload.html#a72ae00e6604850c8798c5e0d825ee7e4">IWorkload</a>.</p>
+
+<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html#l00484">484</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> {</div>
+<div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>(<span class="stringliteral">"NeonUnidirectionalSequenceLstmFloatWorkload_Execute"</span>);</div>
+<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">if</span> (m_Permute1)</div>
+<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div>
+<div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  m_Permute1->run();</div>
+<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  }</div>
+<div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">if</span> (m_Splitter)</div>
+<div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {</div>
+<div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  m_Splitter->run();</div>
+<div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  }</div>
+<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < m_Layers.size(); ++i)</div>
+<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  {</div>
+<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  m_Layers[i]->run();</div>
+<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  }</div>
+<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keywordflow">if</span> (m_Concat)</div>
+<div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  {</div>
+<div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  m_Concat->run();</div>
+<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  }</div>
+<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">if</span> (m_Permute2)</div>
+<div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  {</div>
+<div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  m_Permute2->run();</div>
+<div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  }</div>
+<div class="line"><a name="l00507"></a><span class="lineno"> 507</span> }</div>
+</div><!-- fragment -->
+<p class="reference">References <a class="el" href="_neon_workload_utils_8hpp_source.html#l00032">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>.</p>
+
+</div>
+</div>
+<hr/>The documentation for this class was generated from the following files:<ul>
+<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a></li>
+<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a></li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<div class="ttc" id="anamespacearmnn_html_a7863c179ff92feec660c48ab7b95ae55"><div class="ttname"><a href="namespacearmnn.html#a7863c179ff92feec660c48ab7b95ae55">armnn::ConcatDescriptor</a></div><div class="ttdeci">OriginsDescriptor ConcatDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_fwd_8hpp_source.html#l00059">DescriptorsFwd.hpp:59</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a8cbabc875597b3bed0ccdc0adb289fde"><div class="ttname"><a href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">armnn::ComputeSplitAxis</a></div><div class="ttdeci">std::set< unsigned int > ComputeSplitAxis(const armnn::SplitterDescriptor &desc, const TensorShape &input)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00246">ArmComputeUtils.hpp:246</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a611208865d55ea576cc89ac86d7c19b7"><div class="ttname"><a href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">armnn::InitializeArmComputeTensorData</a></div><div class="ttdeci">void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, TensorInfo tensorInfo, const ITensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.html#l00068">NeonWorkloadUtils.hpp:68</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_aa1e93ef5f9ee3dbb5e7faa9578f180ae"><div class="ttname"><a href="namespacearmnn.html#aa1e93ef5f9ee3dbb5e7faa9578f180ae">armnn::ConvertLstmActivationFuncToAclLayerInfo</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00118">ArmComputeUtils.hpp:118</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div>
+<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00027">WorkloadData.hpp:27</a></div></div>
+<div class="ttc" id="a_profiling_8hpp_html_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.html#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a></div><div class="ttdeci">#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.html#l00227">Profiling.hpp:227</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_base_workload_html_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00067">Workload.hpp:67</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_base_workload_html_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00089">Workload.hpp:89</a></div></div>
+<div class="ttc" id="a_neon_workload_utils_8hpp_html_a7f97eedf3c9436b110df92c947bbb55d"><div class="ttname"><a href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID(label)</div><div class="ttdoc">Creates a profiling event that uses GetGuid() and GetName() from the calling class.</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.html#l00032">NeonWorkloadUtils.hpp:32</a></div></div>
+<div class="ttc" id="anamespacearmnn_utils_html_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.html#l00021">TensorUtils.cpp:21</a></div></div>
+<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00026">WorkloadData.hpp:26</a></div></div>
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