21.02/classarmnn_1_1_ref_q_lstm_workload.xhtml


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275

<!-- Copyright (c) 2020 ARM Limited. -->
<!--                                 -->
<!-- SPDX-License-Identifier: MIT    -->
<!--                                 -->
<!-- HTML header for doxygen 1.8.13-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.13"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>ArmNN: RefQLstmWorkload Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
  $(document).ready(initResizable);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({
    extensions: ["tex2jax.js"],
    jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
 <tbody>
 <tr style="height: 56px;">
  <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/>
  <td style="padding-left: 0.5em;">
   <div id="projectname">
   &#160;<span id="projectnumber">21.02</span>
   </div>
  </td>
 </tr>
 </tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.13 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
$(function() {
  initMenu('',true,false,'search.php','Search');
  $(document).ready(function() { init_search(); });
});
</script>
<div id="main-nav"></div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
  <div id="nav-tree">
    <div id="nav-tree-contents">
      <div id="nav-sync" class="sync"></div>
    </div>
  </div>
  <div id="splitbar" style="-moz-user-select:none;" 
       class="ui-resizable-handle">
  </div>
</div>
<script type="text/javascript">
$(document).ready(function(){initNavTree('classarmnn_1_1_ref_q_lstm_workload.xhtml','');});
</script>
<div id="doc-content">
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
     onmouseover="return searchBox.OnSearchSelectShow()"
     onmouseout="return searchBox.OnSearchSelectHide()"
     onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>

<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0" 
        name="MSearchResults" id="MSearchResults">
</iframe>
</div>

<div class="header">
  <div class="summary">
<a href="#pub-methods">Public Member Functions</a> &#124;
<a href="classarmnn_1_1_ref_q_lstm_workload-members.xhtml">List of all members</a>  </div>
  <div class="headertitle">
<div class="title">RefQLstmWorkload Class Reference</div>  </div>
</div><!--header-->
<div class="contents">

<p><code>#include &lt;<a class="el" href="_ref_q_lstm_workload_8hpp_source.xhtml">RefQLstmWorkload.hpp</a>&gt;</code></p>
<div class="dynheader">
Inheritance diagram for RefQLstmWorkload:</div>
<div class="dyncontent">
 <div class="center">
  <img src="classarmnn_1_1_ref_q_lstm_workload.png" usemap="#RefQLstmWorkload_map" alt=""/>
  <map id="RefQLstmWorkload_map" name="RefQLstmWorkload_map">
<area href="classarmnn_1_1_base_workload.xhtml" alt="BaseWorkload&lt; QLstmQueueDescriptor &gt;" shape="rect" coords="0,56,249,80"/>
<area href="classarmnn_1_1_i_workload.xhtml" title="Workload interface to enqueue a layer computation. " alt="IWorkload" shape="rect" coords="0,0,249,24"/>
</map>
 </div></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:a143c3dfe9ce674a65bb5b93f91ec54e3"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#a143c3dfe9ce674a65bb5b93f91ec54e3">RefQLstmWorkload</a> (const <a class="el" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a> &amp;descriptor, const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &amp;<a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</td></tr>
<tr class="separator:a143c3dfe9ce674a65bb5b93f91ec54e3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae071e8822437c78baea75c3aef3a263a"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">Execute</a> () const override</td></tr>
<tr class="separator:ae071e8822437c78baea75c3aef3a263a"><td class="memSeparator" colspan="2">&#160;</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="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload&lt; QLstmQueueDescriptor &gt;</a></td></tr>
<tr class="memitem:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#af2ef420610280dc5a661cd3d4836d5a2">BaseWorkload</a> (const <a class="el" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a> &amp;descriptor, const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &amp;info)</td></tr>
<tr class="separator:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a81627f96ba06d76e147f7d392a8117ed">PostAllocationConfigure</a> () override</td></tr>
<tr class="separator:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</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_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a965cf380c7adf547d0f14b3f6d1da249">GetData</a> () const</td></tr>
<tr class="separator:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a29c2c6dd77c6fe10674fc6876751cbce inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">profiling::ProfilingGuid</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a29c2c6dd77c6fe10674fc6876751cbce">GetGuid</a> () const final</td></tr>
<tr class="separator:a29c2c6dd77c6fe10674fc6876751cbce inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</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="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarmnn_1_1_i_workload.xhtml">IWorkload</a></td></tr>
<tr class="memitem:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.xhtml#a69c83c02ae8de866bc7a46c49e69c1ba">~IWorkload</a> ()</td></tr>
<tr class="separator:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.xhtml#ab81312bd5e64cbae2803de9f243bdb32">RegisterDebugCallback</a> (const <a class="el" href="namespacearmnn.xhtml#a15f3ad9b5e4e3d46b0a6dda246a7bc28">DebugCallbackFunction</a> &amp;)</td></tr>
<tr class="separator:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2">&#160;</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="-"/>&#160;Protected Attributes inherited from <a class="el" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload&lt; QLstmQueueDescriptor &gt;</a></td></tr>
<tr class="memitem:a0a487c549c63319505095b855ea3c195 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a></td></tr>
<tr class="separator:a0a487c549c63319505095b855ea3c195 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a902044db290170b8467ed4697b7fed24 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">profiling::ProfilingGuid</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a902044db290170b8467ed4697b7fed24">m_Guid</a></td></tr>
<tr class="separator:a902044db290170b8467ed4697b7fed24 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</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="_ref_q_lstm_workload_8hpp_source.xhtml#l00016">16</a> of file <a class="el" href="_ref_q_lstm_workload_8hpp_source.xhtml">RefQLstmWorkload.hpp</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a143c3dfe9ce674a65bb5b93f91ec54e3"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a143c3dfe9ce674a65bb5b93f91ec54e3">&#9670;&nbsp;</a></span>RefQLstmWorkload()</h2>

<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname"><a class="el" href="classarmnn_1_1_ref_q_lstm_workload.xhtml">RefQLstmWorkload</a> </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a> &amp;&#160;</td>
          <td class="paramname"><em>descriptor</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &amp;&#160;</td>
          <td class="paramname"><em>info</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">explicit</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p class="definition">Definition at line <a class="el" href="_ref_q_lstm_workload_8cpp_source.xhtml#l00016">16</a> of file <a class="el" href="_ref_q_lstm_workload_8cpp_source.xhtml">RefQLstmWorkload.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;        : BaseWorkload&lt;QLstmQueueDescriptor&gt;(descriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;        , m_InputToInputWeightsTensor     (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToInputWeights))</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;        , m_InputToForgetWeightsTensor    (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToForgetWeights))</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;        , m_InputToCellWeightsTensor      (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToCellWeights))</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;        , m_InputToOutputWeightsTensor    (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToOutputWeights))</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;        , m_RecurrentToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToInputWeights))</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;        , m_RecurrentToForgetWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToForgetWeights))</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;        , m_RecurrentToCellWeightsTensor  (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToCellWeights))</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;        , m_RecurrentToOutputWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToOutputWeights))</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;        , m_CellToInputWeightsTensor      (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToInputWeights))</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;        , m_CellToForgetWeightsTensor     (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToForgetWeights))</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;        , m_CellToOutputWeightsTensor     (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToOutputWeights))</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;        , m_InputGateBiasTensor           (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputGateBias))</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;        , m_ForgetGateBiasTensor          (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetGateBias))</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;        , m_CellBiasTensor                (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellBias))</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;        , m_OutputGateBiasTensor          (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputGateBias))</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;        , m_ProjectionWeightsTensor       (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionWeights))</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;        , m_ProjectionBiasTensor          (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionBias))</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;        , m_InputLayerNormWeightsTensor   (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputLayerNormWeights))</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;        , m_ForgetLayerNormWeightsTensor  (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetLayerNormWeights))</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;        , m_CellLayerNormWeightsTensor    (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellLayerNormWeights))</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        , m_OutputLayerNormWeightsTensor  (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputLayerNormWeights))</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;{}</div><div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1aef5d233d1f569f34d6c36bdf5ae9e5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a></div><div class="ttdeci">std::unique_ptr&lt; armnn::ScopedCpuTensorHandle &gt; AssignScopedCpuTensorHandle(const armnn::ConstCpuTensorHandle *ptr)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00299">LstmUtils.cpp:299</a></div></div>
</div><!-- fragment -->
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a id="ae071e8822437c78baea75c3aef3a263a"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae071e8822437c78baea75c3aef3a263a">&#9670;&nbsp;</a></span>Execute()</h2>

<div class="memitem">
<div class="memproto">
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void Execute </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Implements <a class="el" href="classarmnn_1_1_i_workload.xhtml#a72ae00e6604850c8798c5e0d825ee7e4">IWorkload</a>.</p>

<p class="definition">Definition at line <a class="el" href="_ref_q_lstm_workload_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="_ref_q_lstm_workload_8cpp_source.xhtml">RefQLstmWorkload.cpp</a>.</p>

<p class="reference">References <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::Activation</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00229">ClipVector()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00244">CopyVector()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00187">TensorInfo::GetShape()</a>, <a class="el" href="_ref_workload_utils_8hpp_source.xhtml#l00026">armnn::GetTensorInfo()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01187">QLstmDescriptor::m_CellClip</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01203">QLstmDescriptor::m_CellIntermediateScale</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01191">QLstmDescriptor::m_CifgEnabled</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00046">BaseWorkload&lt; QLstmQueueDescriptor &gt;::m_Data</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01201">QLstmDescriptor::m_ForgetIntermediateScale</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01209">QLstmDescriptor::m_HiddenStateScale</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01207">QLstmDescriptor::m_HiddenStateZeroPoint</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01199">QLstmDescriptor::m_InputIntermediateScale</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00030">QueueDescriptor::m_Inputs</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01197">QLstmDescriptor::m_LayerNormEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01205">QLstmDescriptor::m_OutputIntermediateScale</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00031">QueueDescriptor::m_Outputs</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00057">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01193">QLstmDescriptor::m_PeepholeEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01189">QLstmDescriptor::m_ProjectionClip</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01195">QLstmDescriptor::m_ProjectionEnabled</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00087">MatrixBatchVectorMultiplyAccumulate()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00040">MeanStddevNormalization()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::QAsymmS8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::Sigmoid</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::Signed32</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00173">Sub1Vector()</a>, <a class="el" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::TanH</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00016">VectorBatchVectorAdd()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00113">VectorBatchVectorAssign()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00152">VectorBatchVectorCwiseProduct()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00131">VectorBatchVectorCwiseProductAccumulate()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00187">VectorVectorCwiseProduct()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00204">VectorVectorCwiseProductAccumulate()</a>, and <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00076">ZeroVector()</a>.</p>
<div class="fragment"><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;{</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    <span class="comment">// This is a porting of the QLSTM::Execute() method in the Android code base</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    <span class="comment">// Note: this implementation wraps the arithmetic functions of the LSTM cell in Quantize/Dequantize ops, so all</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <span class="comment">// computation is done in the floating point domain. Arithmetic functions are found in LstmUtils.cpp.</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    <span class="comment">// Refer to: android/frameworks/ml/nn/common/operations/QLSTM.cpp</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>&amp; internalType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; inputInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]);</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; outputStateInInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1]);</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; cellStateInInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2]);</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; outputStateOutInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]);</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; cellStateOutInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]);</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keyword">const</span> TensorInfo&amp; outputInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]);</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keyword">const</span> TensorShape&amp; inputShape = inputInfo.GetShape();</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    <span class="keyword">const</span> TensorShape&amp; outputStateInShape = outputStateInInfo.GetShape();</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keyword">const</span> TensorShape&amp; cellStateInShape = cellStateInInfo.GetShape();</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <span class="comment">// Infer numBatches, inputSize, outputSize and numUnits</span></div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <span class="keyword">const</span> uint32_t numBatches = inputShape[0];</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="keyword">const</span> uint32_t inputSize  = inputShape[1];</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = outputStateInShape[1];</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    <span class="keyword">const</span> uint32_t numUnits   = cellStateInShape[1];</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="comment">// Optional param settings</span></div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> cifgEnabled      = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> peepholeEnabled  = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> projectionEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> layerNormEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="comment">// Input decoders</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputDecoder =</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;            MakeDecoder&lt;float&gt;(inputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]-&gt;Map());</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateInDecoder =</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;            MakeDecoder&lt;float&gt;(outputStateInInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1]-&gt;Map());</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateInDecoder =</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;            MakeDecoder&lt;float&gt;(cellStateInInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2]-&gt;Map());</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="comment">// Output decoders</span></div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateOutDecoder =</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;            MakeDecoder&lt;float&gt;(outputStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]-&gt;Map());</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateOutDecoder =</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;            MakeDecoder&lt;float&gt;(cellStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]-&gt;Map());</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputDecoder =</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;            MakeDecoder&lt;float&gt;(outputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]-&gt;Map());</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <span class="comment">// Output encoders</span></div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputStateOutEncoder =</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;            MakeEncoder&lt;float&gt;(outputStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]-&gt;Map());</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellStateOutEncoder =</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;            MakeEncoder&lt;float&gt;(cellStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]-&gt;Map());</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputEncoder =</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;            MakeEncoder&lt;float&gt;(outputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]-&gt;Map());</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="comment">// Weights decoders</span></div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;            m_InputToForgetWeightsTensor-&gt;GetTensorInfo(), m_InputToForgetWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToCellWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;            m_InputToCellWeightsTensor-&gt;GetTensorInfo(), m_InputToCellWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;            m_InputToOutputWeightsTensor-&gt;GetTensorInfo(), m_InputToOutputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;            m_RecurrentToForgetWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToForgetWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToCellWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;            m_RecurrentToCellWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToCellWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;            m_RecurrentToOutputWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToOutputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="comment">// Optional CIFG params</span></div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToInputWeightsDecoder;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToInputWeightsDecoder;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateBiasDecoder;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="comment">// Optional Peephole params</span></div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToInputWeightsDecoder;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToForgetWeightsDecoder;</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToOutputWeightsDecoder;</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="comment">// Optional Projection params</span></div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionWeightsDecoder;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionBiasDecoder;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="comment">// Optional Layer Norm params</span></div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputLayerNormWeightsDecoder;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetLayerNormWeightsDecoder;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellLayerNormWeightsDecoder;</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputLayerNormWeightsDecoder;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="comment">// Biases are only used when Layer Norm is enabled. Scale is defined as (XLayerNormWeights Scale / 1024)</span></div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateBiasDecoder;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellGateBiasDecoder;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateBiasDecoder;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="comment">// Int16 vectors for internal state data (to be decoded/encoded)</span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keyword">const</span> uint32_t stateTensorSize = numBatches * numUnits;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    std::vector&lt;int16_t&gt; inputGateData(stateTensorSize);</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    std::vector&lt;int16_t&gt; cellGateData(stateTensorSize);</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    std::vector&lt;int16_t&gt; forgetGateData(stateTensorSize);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    std::vector&lt;int16_t&gt; outputGateData(stateTensorSize);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    std::vector&lt;int32_t&gt; hiddenStateData(stateTensorSize);</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    std::vector&lt;int16_t&gt; outputInt16Data(numBatches * outputSize);</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateInfo(</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;            {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>, 0);</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellGateInfo(</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;            {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>, 0);</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateInfo(</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;            {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a>, 0);</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateInfo(</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;            {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a>, 0);</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> hiddenStateInfo({numBatches, numUnits},</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;                                      <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>,</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                                      <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a>);</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInt16Info({numBatches , outputSize},</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;                                      outputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;                                      outputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    <span class="comment">// Decoders/Encoders for internal states</span></div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateDecoder =</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;            MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellGateDecoder =</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;            MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateDecoder =</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;            MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateDecoder =</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;            MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; hiddenStateDecoder =</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;            MakeDecoder&lt;float&gt;(hiddenStateInfo, hiddenStateData.data());</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; inputGateEncoder =</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;            MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellGateEncoder =</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;            MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; forgetGateEncoder =</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;            MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputGateEncoder =</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;            MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; hiddenStateEncoder =</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;            MakeEncoder&lt;float&gt;(hiddenStateInfo, hiddenStateData.data());</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="comment">// Int16 used to accumulate output to prevent overflowing (after Projection MatMul)</span></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputInt16Decoder =</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;            MakeDecoder&lt;float&gt;(outputInt16Info, outputInt16Data.data());</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputInt16Encoder =</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;            MakeEncoder&lt;float&gt;(outputInt16Info, outputInt16Data.data());</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <span class="comment">// Create decoders for optional params if they are enabled</span></div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    {</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        inputToInputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                m_InputToInputWeightsTensor-&gt;GetTensorInfo(), m_InputToInputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        recurrentToInputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;                m_RecurrentToInputWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToInputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    }</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    {</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        {</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;            cellToInputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;                    m_CellToInputWeightsTensor-&gt;GetTensorInfo(), m_CellToInputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        }</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        cellToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                m_CellToForgetWeightsTensor-&gt;GetTensorInfo(), m_CellToForgetWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        cellToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;                m_CellToOutputWeightsTensor-&gt;GetTensorInfo(), m_CellToOutputWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    }</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <span class="keywordflow">if</span> (projectionEnabled)</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    {</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        projectionWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                m_ProjectionWeightsTensor-&gt;GetTensorInfo(), m_ProjectionWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        {</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;            projectionBiasDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                    m_ProjectionBiasTensor-&gt;GetTensorInfo(), m_ProjectionBiasTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;        }</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    }</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    {</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        {</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;            inputLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;                    m_InputLayerNormWeightsTensor-&gt;GetTensorInfo(), m_InputLayerNormWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;            <span class="comment">// Bias only used if layer norm enabled</span></div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;                    m_InputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;            inputGateBiasDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;                    inputGateBiasTensorInfo, m_InputGateBiasTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        }</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        forgetLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;                m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo(), m_ForgetLayerNormWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        cellLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                m_CellLayerNormWeightsTensor-&gt;GetTensorInfo(), m_CellLayerNormWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        outputLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;                m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo(), m_OutputLayerNormWeightsTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="comment">// Bias only used if layer norm enabled</span></div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        forgetGateBiasDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                forgetGateBiasTensorInfo, m_ForgetGateBiasTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;                m_CellLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        cellGateBiasDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;                cellGateBiasTensorInfo, m_CellBiasTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;                m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        outputGateBiasDecoder = MakeDecoder&lt;float&gt;(</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;                outputGateBiasTensorInfo, m_OutputGateBiasTensor-&gt;GetTensor&lt;<span class="keywordtype">void</span>&gt;());</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    }</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <span class="comment">// Initialize internal state tensors with zeroes.</span></div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    {</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*inputGateEncoder, stateTensorSize);</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    }</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*forgetGateEncoder, stateTensorSize);</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*cellGateEncoder, stateTensorSize);</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*outputGateEncoder, stateTensorSize);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*hiddenStateEncoder, stateTensorSize);</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="comment">// Input weights * Input</span></div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    {</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToInputWeightsDecoder,</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                                            numUnits, inputSize, *inputDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    }</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToForgetWeightsDecoder,</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;                                        numUnits, inputSize, *inputDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToCellWeightsDecoder,</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;                                        numUnits, inputSize, *inputDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToOutputWeightsDecoder,</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;                                        numUnits, inputSize, *inputDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <span class="comment">// Recurrent weights * OutputStateIn</span></div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    {</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToInputWeightsDecoder,</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;                                            numUnits, outputSize, *outputStateInDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    }</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToForgetWeightsDecoder,</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;                                        numUnits, outputSize, *outputStateInDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToCellWeightsDecoder,</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;                                        numUnits, outputSize, *outputStateInDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToOutputWeightsDecoder,</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;                                        numUnits, outputSize, *outputStateInDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <span class="comment">// Input gate.</span></div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    {</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;        {</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToInputWeightsDecoder,</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                                                    numUnits, *cellStateInDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;        }</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;        {</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;            inputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;                                               m_InputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;                                               1024);</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;            inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*inputGateDecoder,</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                                    *inputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;            inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*inputLayerNormWeightsDecoder,</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;                                          numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;            inputGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;            inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*inputGateBiasDecoder,</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;                                 numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;            inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        }</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;        inputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;        inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;        <span class="comment">// Input gate sigmoid</span></div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*inputGateDecoder, *inputGateEncoder,</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                   TensorInfo({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                   <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;        inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    }</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    <span class="comment">// Forget gate</span></div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    {</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToForgetWeightsDecoder, numUnits,</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;                                                *cellStateInDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    }</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    {</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        <span class="comment">// Quantize layer norm output to Input Scale * m_ForgetLayerNormWeightsTensor * 1024</span></div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        forgetGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;                                            m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;                                            1024);</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*forgetGateDecoder,</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;                                *forgetGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;        forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*forgetLayerNormWeightsDecoder,</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;                                      numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        <span class="comment">// Dequantize layer norm output to (1 / 4096)</span></div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        forgetGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*forgetGateBiasDecoder,</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                             numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;        forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    }</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    forgetGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;    <span class="comment">// Forget gate sigmoid</span></div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*forgetGateDecoder, *forgetGateEncoder,</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;               TensorInfo({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;               <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="comment">// Cell (Modulation) gate</span></div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    {</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        cellGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;                                          m_CellLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;                                          1024);</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*cellGateDecoder, *cellGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*cellLayerNormWeightsDecoder,</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;                                      numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        cellGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;        cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*cellGateBiasDecoder,</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;                             numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    }</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    cellGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    <span class="comment">// Cell (Modulation) gate tanH</span></div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellGateDecoder, *cellGateEncoder,</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;               TensorInfo({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;               <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*forgetGateDecoder, *cellStateInDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    <span class="keywordflow">if</span> (cifgEnabled)</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    {</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a>(*forgetGateDecoder, stateTensorSize, *forgetGateEncoder);</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;                *cellGateDecoder, *forgetGateDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    }</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    {</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;                *cellGateDecoder, *inputGateDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;    }</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    <span class="comment">// Final cell state out calculated here</span></div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> &gt; 0.0)</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    {</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*cellStateOutDecoder, stateTensorSize, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>, *cellStateOutEncoder);</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    }</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <span class="comment">// Output gate.</span></div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    {</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToOutputWeightsDecoder,</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;                                                numUnits, *cellStateOutDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    }</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    {</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        outputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;                                            m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;                                            1024);</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*outputGateDecoder, *outputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*outputLayerNormWeightsDecoder, numUnits, *outputGateDecoder,</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;                                      numBatches, *outputGateEncoder);</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        outputGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*outputGateBiasDecoder, numUnits, *outputGateDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;        outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    }</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    outputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    <span class="comment">// Output gate sigmoid</span></div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*outputGateDecoder, *outputGateEncoder,</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;               TensorInfo({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;               <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    <span class="comment">// Hidden state tanH</span></div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellStateOutDecoder, *cellGateEncoder,</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;               TensorInfo({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;               <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    <span class="comment">// Final hidden state output</span></div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*outputGateDecoder, *cellGateDecoder, stateTensorSize, *hiddenStateEncoder);</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    <span class="comment">// Projection</span></div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    {</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;        <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;        {</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*projectionBiasDecoder, outputSize, numBatches, *outputInt16Encoder);</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;        }</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*projectionWeightsDecoder, outputSize, numUnits, *hiddenStateDecoder,</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;                                            numBatches, *outputInt16Encoder);</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputInt16Decoder, numBatches * outputSize, *outputEncoder);</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> &gt; 0.0)</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        {</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;            <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*outputDecoder, numBatches * outputSize, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a>, *outputEncoder);</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        }</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    }</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    {</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;        <span class="comment">// Output has same quantization scale as hidden state if projection is disabled</span></div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*hiddenStateDecoder, numBatches * outputSize, *outputEncoder);</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    }</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    <span class="comment">// output == outputStateOut</span></div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputDecoder, numBatches * outputSize, *outputStateOutEncoder);</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;}</div><div class="ttc" id="_lstm_utils_8cpp_xhtml_a0ed27dd6d6125a06bf654080f4184360"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a></div><div class="ttdeci">void MeanStddevNormalization(armnn::Decoder&lt; float &gt; &amp;input_vector, armnn::Encoder&lt; float &gt; &amp;output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00040">LstmUtils.cpp:40</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a389c4bbafd0fff7060cbb183f20a2134"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a></div><div class="ttdeci">void VectorBatchVectorAdd(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00016">LstmUtils.cpp:16</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a117781e8e9b7321722bbdd8ff74b484a"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a></div><div class="ttdeci">void ClipVector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, float absLimit, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00229">LstmUtils.cpp:229</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::QLstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01193">Descriptors.hpp:1193</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_aca7bd1dff180b6a5de894537f8220793"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a></div><div class="ttdeci">void Sub1Vector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;result)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00173">LstmUtils.cpp:173</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">armnn::QLstmDescriptor::m_HiddenStateScale</a></div><div class="ttdeci">float m_HiddenStateScale</div><div class="ttdoc">Hidden State quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01209">Descriptors.hpp:1209</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_a0a487c549c63319505095b855ea3c195"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">armnn::BaseWorkload&lt; QLstmQueueDescriptor &gt;::m_Data</a></div><div class="ttdeci">const QLstmQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00046">Workload.hpp:46</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">armnn::QLstmDescriptor::m_OutputIntermediateScale</a></div><div class="ttdeci">float m_OutputIntermediateScale</div><div class="ttdoc">Output intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01205">Descriptors.hpp:1205</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a00d8a623c04f1120f6fee3fe38d1cee9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a></div><div class="ttdeci">void CopyVector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00244">LstmUtils.cpp:244</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a796323e16216b880043dc5ebbaa2372b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorBatchVectorCwiseProductAccumulate(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00131">LstmUtils.cpp:131</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a4c20bc573b70e89327b334f924da97b5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a></div><div class="ttdeci">void ZeroVector(armnn::Encoder&lt; float &gt; &amp;vector, uint32_t vSize)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00076">LstmUtils.cpp:76</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a5b81dc0a1a9a2bccab8bb79dfa3e77b7"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a></div><div class="ttdeci">void VectorVectorCwiseProduct(armnn::Decoder&lt; float &gt; &amp;vector1, armnn::Decoder&lt; float &gt; &amp;vector2, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00187">LstmUtils.cpp:187</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00057">WorkloadData.hpp:57</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1d7ad9698b02282a57fdb17b3af745f9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a></div><div class="ttdeci">void VectorBatchVectorCwiseProduct(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00152">LstmUtils.cpp:152</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_ab93a2c78551c3d3aba8ddcafb792a36d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a></div><div class="ttdeci">void MatrixBatchVectorMultiplyAccumulate(armnn::Decoder&lt; float &gt; &amp;matrix, uint32_t mRows, uint32_t mCols, armnn::Decoder&lt; float &gt; &amp;vector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00087">LstmUtils.cpp:87</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::QLstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01197">Descriptors.hpp:1197</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa6a518b65088f34803b3214334bdff61"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">armnn::QLstmDescriptor::m_ProjectionClip</a></div><div class="ttdeci">float m_ProjectionClip</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01189">Descriptors.hpp:1189</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">armnn::QLstmDescriptor::m_InputIntermediateScale</a></div><div class="ttdeci">float m_InputIntermediateScale</div><div class="ttdoc">Input intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01199">Descriptors.hpp:1199</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a2e653f948d93f4177f267a7b1b4ed47d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorVectorCwiseProductAccumulate(armnn::Decoder&lt; float &gt; &amp;vector1, armnn::Decoder&lt; float &gt; &amp;vector2, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00204">LstmUtils.cpp:204</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a8c4a96233c9b62c76d611316da11124b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a></div><div class="ttdeci">void VectorBatchVectorAssign(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outBatchVector)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00113">LstmUtils.cpp:113</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">armnn::QLstmDescriptor::m_ForgetIntermediateScale</a></div><div class="ttdeci">float m_ForgetIntermediateScale</div><div class="ttdoc">Forget intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01201">Descriptors.hpp:1201</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ac81fb0e66dc623dc37c77f219f53a6d3"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">armnn::QLstmDescriptor::m_CellClip</a></div><div class="ttdeci">float m_CellClip</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01187">Descriptors.hpp:1187</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::QLstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01195">Descriptors.hpp:1195</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_af7ec4c0fa4375a45a70e4e31f3d8af47"><div class="ttname"><a href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo &amp; GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers </div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.xhtml#l00026">RefWorkloadUtils.hpp:26</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">armnn::QLstmDescriptor::m_CellIntermediateScale</a></div><div class="ttdeci">float m_CellIntermediateScale</div><div class="ttdoc">Cell intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01203">Descriptors.hpp:1203</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::QLstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable CIFG (coupled input &amp; forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01191">Descriptors.hpp:1191</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">armnn::QLstmDescriptor::m_HiddenStateZeroPoint</a></div><div class="ttdeci">int32_t m_HiddenStateZeroPoint</div><div class="ttdoc">Hidden State zero point. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01207">Descriptors.hpp:1207</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div></div>
</div><!-- fragment -->
</div>
</div>
<hr/>The documentation for this class was generated from the following files:<ul>
<li>src/backends/reference/workloads/<a class="el" href="_ref_q_lstm_workload_8hpp_source.xhtml">RefQLstmWorkload.hpp</a></li>
<li>src/backends/reference/workloads/<a class="el" href="_ref_q_lstm_workload_8cpp_source.xhtml">RefQLstmWorkload.cpp</a></li>
</ul>
</div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
  <ul>
    <li class="navelem"><a class="el" href="namespacearmnn.xhtml">armnn</a></li><li class="navelem"><a class="el" href="classarmnn_1_1_ref_q_lstm_workload.xhtml">RefQLstmWorkload</a></li>
    <li class="footer">Generated on Thu Feb 25 2021 17:28:00 for ArmNN by
    <a href="http://www.doxygen.org/index.html">
    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
  </ul>
</div>
</body>
</html>