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
|
<!-- 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: README.md Source File</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">
 <span id="projectnumber">22.05</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('_r_e_a_d_m_e_8md.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="headertitle">
<div class="title">README.md</div> </div>
</div><!--header-->
<div class="contents">
<a href="_r_e_a_d_m_e_8md.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> # Arm NN</div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> </div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> * [Quick Start Guides](#quick-start-guides)</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> * [Pre-Built Binaries](#pre-built-binaries)</div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> * [Software Overview](#software-overview)</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> * [Get Involved](#get-involved)</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> * [Contributions](#contributions)</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> * [Disclaimer](#disclaimer)</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> * [License](#license)</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> * [Third-Party](#third-party)</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> * [Build Flags](#build-flags)</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> **_Arm NN_** is the **most performant** machine learning (ML) inference engine for Android and Linux, accelerating ML</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> on **Arm Cortex-A CPUs and Arm Mali GPUs**. This ML inference engine is an open source SDK which bridges the gap</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> between existing neural network frameworks and power-efficient Arm IP.</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> Arm NN outperforms generic ML libraries due to **Arm architecture-specific optimizations** (e.g. SVE2) by utilizing</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> **[Arm Compute Library (ACL)](https://github.com/ARM-software/ComputeLibrary/)**. To target Arm Ethos-N NPUs, Arm NN</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> utilizes the [Ethos-N NPU Driver](https://github.com/ARM-software/ethos-n-driver-stack). For Arm Cortex-M acceleration,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> please see [CMSIS-NN](https://github.com/ARM-software/CMSIS_5).</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> Arm NN is written using portable **C++14** and built using [CMake](https://cmake.org/) - enabling builds for a wide</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> variety of target platforms, from a wide variety of host environments. **Python** developers can interface with Arm NN</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> through the use of our **Arm NN TF Lite Delegate**.</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> ## Quick Start Guides</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> **The Arm NN TF Lite Delegate provides the widest ML operator support in Arm NN** and is an easy way to accelerate</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> your ML model. To start using the TF Lite Delegate, first download the **[Pre-Built Binaries](#pre-built-binaries)** for</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> the latest release of Arm NN. Using a Python interpreter, you can load your TF Lite model into the Arm NN TF Lite</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> Delegate and run accelerated inference. Please see this</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> **[Quick Start Guide](delegate/DelegateQuickStartGuide.md)** on GitHub or this more comprehensive</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> **[Arm Developer Guide](https://developer.arm.com/documentation/102561/latest/)** for information on how to accelerate</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> your TF Lite model using the Arm NN TF Lite Delegate.</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> The fastest way to integrate Arm NN into an **Android app** is by using our **Arm NN AAR (Android Archive) file with</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> Android Studio**. The AAR file nicely packages up the Arm NN TF Lite Delegate, Arm NN itself and ACL; ready to be</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> integrated into your Android ML application. Using the AAR allows you to benefit from the **vast operator support** of</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> the Arm NN TF Lite Delegate. We held an **[Arm AI Tech Talk](https://www.youtube.com/watch?v=Zu4v0nqq2FA)** on how to</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> accelerate an ML Image Segmentation app in 5 minutes using this AAR file, with the supporting guide</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> **[here](https://developer.arm.com/documentation/102744/latest)**. To download the Arm NN AAR file, please see the</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> **[Pre-Built Binaries](#pre-built-binaries)** section below.</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> We also provide Debian packages for Arm NN, which are a quick way to start using Arm NN and the TF Lite Parser</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> (albeit with less ML operator support than the TF Lite Delegate). There is an installation guide available</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> [here](InstallationViaAptRepository.md) which provides instructions on how to install the Arm NN Core and the TF Lite</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> Parser for Ubuntu 20.04.</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> ## Pre-Built Binaries</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> | Operating System | Architecture-specific Release Archive (Download) |</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> |-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> | Android (AAR) | [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmnnDelegate-release.aar) |</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> | Android 27 | [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-27-arm64-v8.2-a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-27-arm64-v8a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-27-armv7a.tar.gz) |</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> | Android 28 | [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-28-arm64-v8.2-a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-28-arm64-v8a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-28-armv7a.tar.gz) |</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> | Android 29 | [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-29-arm64-v8.2-a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-29-arm64-v8a.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-android-29-armv7a.tar.gz) |</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> | Linux | [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-linux-aarch64.tar.gz) [](https://github.com/ARM-software/armnn/releases/download/v22.02/ArmNN-linux-x86_64.tar.gz) |</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> ## Software Overview</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> The Arm NN SDK supports ML models in **TensorFlow Lite** (TF Lite) and **ONNX** formats.</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> **Arm NN's TF Lite Delegate** accelerates TF Lite models through **Python or C++ APIs**. Supported TF Lite operators</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> are accelerated by Arm NN and any unsupported operators are delegated (fallback) to the reference TF Lite runtime -</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> ensuring extensive ML operator support. **The recommended way to use Arm NN is to</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> [convert your model to TF Lite format](https://www.tensorflow.org/lite/convert) and use the TF Lite Delegate.** Please</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> refer to the [Quick Start Guides](#quick-start-guides) for more information on how to use the TF Lite Delegate.</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> Arm NN also provides **TF Lite and ONNX parsers** which are C++ libraries for integrating TF Lite or ONNX models</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> into your ML application. Please note that these parsers do not provide extensive ML operator coverage as compared</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> to the Arm NN TF Lite Delegate.</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> **Android** ML application developers have a number of options for using Arm NN:</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> * Use our Arm NN AAR (Android Archive) file with **Android Studio** as described in the</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> [Quick Start Guides](#quick-start-guides) section</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> * Download and use our [Pre-Built Binaries](#pre-built-binaries) for the Android platform</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> * Build Arm NN from scratch with the Android NDK using this [GitHub guide](BuildGuideAndroidNDK.md)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> Arm also provides an [Android-NN-Driver](https://github.com/ARM-software/android-nn-driver) which implements a</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> hardware abstraction layer (HAL) for the Android NNAPI. When the Android NN Driver is integrated on an Android device,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> ML models used in Android applications will automatically be accelerated by Arm NN.</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> For more information about the Arm NN components, please refer to our</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> [documentation](https://github.com/ARM-software/armnn/wiki/Documentation).</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> Arm NN is a key component of the [machine learning platform](https://mlplatform.org/), which is part of the</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> [Linaro Machine Intelligence Initiative](https://www.linaro.org/news/linaro-announces-launch-of-machine-intelligence-initiative/).</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> For FAQs and troubleshooting advice, see the [FAQ](docs/FAQ.md) or take a look at previous</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> [GitHub Issues](https://github.com/ARM-software/armnn/issues).</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> ## Get Involved</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> The best way to get involved is by using our software. If you need help or encounter and issue, please raise it as a</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> [GitHub Issue](https://github.com/ARM-software/armnn/issues). Feel free to have a look at any of our open issues too.</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> We also welcome feedback on our documentation.</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> Feature requests without a volunteer to implement them are closed, but have the 'Help wanted' label, these can be</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> found [here](https://github.com/ARM-software/armnn/issues?q=is%3Aissue+label%3A%22Help+wanted%22+).</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> Once you find a suitable Issue, feel free to re-open it and add a comment, so that Arm NN engineers know you are</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> working on it and can help.</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> When the feature is implemented the 'Help wanted' label will be removed.</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> ## Contributions</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> The Arm NN project welcomes contributions. For more details on contributing to Arm NN please see the</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> [Contributing page](https://mlplatform.org/contributing/) on the [MLPlatform.org](https://mlplatform.org/) website,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> or see the [Contributor Guide](ContributorGuide.md).</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> Particularly if you'd like to implement your own backend next to our CPU, GPU and NPU backends there are guides for</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> backend development: [Backend development guide](src/backends/README.md),</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> [Dynamic backend development guide](src/dynamic/README.md).</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> ## Disclaimer</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> The armnn/tests directory contains tests used during Arm NN development. Many of them depend on third-party IP, model</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> protobufs and image files not distributed with Arm NN. The dependencies for some tests are available freely on</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> the Internet, for those who wish to experiment, but they won't run out of the box.</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> ## License</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> Arm NN is provided under the [MIT](https://spdx.org/licenses/MIT.html) license.</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> See [LICENSE](LICENSE) for more information. Contributions to this project are accepted under the same license.</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> Individual files contain the following tag instead of the full license text.</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  SPDX-License-Identifier: MIT</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> This enables machine processing of license information based on the SPDX License Identifiers that are available</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> here: http://spdx.org/licenses/</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> ## Third-party</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> Third party tools used by Arm NN:</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> | Tool | License (SPDX ID) | Description | Version | Provenience</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> |----------------|-------------------|------------------------------------------------------------------|-------------|-------------------</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> | cxxopts | MIT | A lightweight C++ option parser library | SHA 12e496da3d486b87fa9df43edea65232ed852510 | https://github.com/jarro2783/cxxopts</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> | doctest | MIT | Header-only C++ testing framework | 2.4.6 | https://github.com/onqtam/doctest</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> | fmt | MIT | {fmt} is an open-source formatting library providing a fast and safe alternative to C stdio and C++ iostreams. | 7.0.1 | https://github.com/fmtlib/fmt</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> | ghc | MIT | A header-only single-file std::filesystem compatible helper library | 1.3.2 | https://github.com/gulrak/filesystem</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> | half | MIT | IEEE 754 conformant 16-bit half-precision floating point library | 1.12.0 | http://half.sourceforge.net</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> | mapbox/variant | BSD | A header-only alternative to 'boost::variant' | 1.1.3 | https://github.com/mapbox/variant</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> | stb | MIT | Image loader, resize and writer | 2.16 | https://github.com/nothings/stb</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> ## Build Flags</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> Arm NN uses the following security related build flags in their code:</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> | Build flags |</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> |---------------------|</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> | -Wall |</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> | -Wextra |</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> | -Wold-style-cast |</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> | -Wno-missing-braces |</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> | -Wconversion |</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> | -Wsign-conversion |</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> | -Werror |</div></div><!-- fragment --></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="_r_e_a_d_m_e_8md.xhtml">README.md</a></li>
<li class="footer">Generated on Tue May 24 2022 11:27:15 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>
|
