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gerrit-public.fairphone.software / platform / external / ComputeLibrary / 6943bb00e79fe2ea4c127dc04b3440c5b0b29ce0 / . / documentation / index.xhtml
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Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]9<title>Compute Library: Introduction</title>
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Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]42 <div id="projectname">Compute Library
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]43 &#160;<span id="projectnumber">17.12</span>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]44 </div>
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Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]111<div class="title">Introduction </div> </div>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]112</div><!--header-->
113<div class="contents">
114<div class="toc"><h3>Table of Contents</h3>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]115<ul><li class="level1"><a href="#S0_1_contact">Contact / Support</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]116<li class="level1"><a href="#S0_2_prebuilt_binaries">Pre-built binaries</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]117<li class="level1"><a href="#S1_file_organisation">File organisation</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]118<li class="level1"><a href="#S2_versions_changelog">Release versions and changelog</a><ul><li class="level2"><a href="#S2_1_versions">Release versions</a></li>
119<li class="level2"><a href="#S2_2_changelog">Changelog</a></li>
120</ul>
121</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]122<li class="level1"><a href="#S3_how_to_build">How to build the library and the examples</a><ul><li class="level2"><a href="#S3_1_build_options">Build options</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]123<li class="level2"><a href="#S3_2_linux">Building for Linux</a><ul><li class="level3"><a href="#S3_2_1_library">How to build the library ?</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]124<li class="level3"><a href="#S3_2_2_examples">How to manually build the examples ?</a></li>
125</ul>
126</li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]127<li class="level2"><a href="#S3_3_android">Building for Android</a><ul><li class="level3"><a href="#S3_3_1_library">How to build the library ?</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]128<li class="level3"><a href="#S3_3_2_examples">How to manually build the examples ?</a></li>
129</ul>
130</li>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]131<li class="level2"><a href="#S3_4_bare_metal">Building for bare metal</a><ul><li class="level3"><a href="#S3_4_1_library">How to build the library ?</a></li>
132<li class="level3"><a href="#S3_4_2_examples">How to manually build the examples ?</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]133</ul>
134</li>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]135<li class="level2"><a href="#S3_5_windows_host">Building on a Windows host system</a><ul><li class="level3"><a href="#S3_5_1_ubuntu_on_windows">Bash on Ubuntu on Windows</a></li>
136<li class="level3"><a href="#S3_5_2_cygwin">Cygwin</a></li>
137</ul>
138</li>
139<li class="level2"><a href="#S3_6_cl_stub_library">The OpenCL stub library</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]140<li class="level2"><a href="#S3_7_gles_stub_library">The Linux OpenGLES and EGL stub libraries</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]141</ul>
142</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]143</ul>
144</div>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]145<div class="textblock"><p>The Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.Several builds of the library are available using various configurations:</p><ul>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]146<li>OS: Linux, Android or bare metal.</li>
147<li>Architecture: armv7a (32bit) or arm64-v8a (64bit)</li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]148<li>Technology: NEON / OpenCL / GLES_COMPUTE / NEON and OpenCL and GLES_COMPUTE</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]149<li>Debug / Asserts / Release: Use a build with asserts enabled to debug your application and enable extra validation. Once you are sure your application works as expected you can switch to a release build of the library for maximum performance.</li>
150</ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]151<h1><a class="anchor" id="S0_1_contact"></a>
152Contact / Support</h1>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]153<p>Please email <a href="#" onclick="location.href='mai'+'lto:'+'dev'+'el'+'ope'+'r@'+'arm'+'.c'+'om'; return false;">devel<span style="display: none;">.nosp@m.</span>oper<span style="display: none;">.nosp@m.</span>@arm.<span style="display: none;">.nosp@m.</span>com</a></p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]154<p>In order to facilitate the work of the support team please provide the build information of the library you are using. To get the version of the library you are using simply run: </p><pre class="fragment">$ strings android-armv7a-cl-asserts/libarm_compute.so | grep arm_compute_version
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]155arm_compute_version=v16.12 Build options: {'embed_kernels': '1', 'opencl': '1', 'arch': 'armv7a', 'neon': '0', 'asserts': '1', 'debug': '0', 'os': 'android', 'Werror': '1'} Git hash=f51a545d4ea12a9059fe4e598a092f1fd06dc858
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]156</pre><h1><a class="anchor" id="S0_2_prebuilt_binaries"></a>
157Pre-built binaries</h1>
158<p>For each release we provide some pre-built binaries of the library <a href="https://github.com/ARM-software/ComputeLibrary/releases">here</a></p>
159<p>These binaries have been built using the following toolchains:</p><ul>
160<li>Linux armv7a: gcc-linaro-arm-linux-gnueabihf-4.9-2014.07_linux</li>
161<li>Linux arm64-v8a: gcc-linaro-4.9-2016.02-x86_64_aarch64-linux-gnu</li>
162<li>Android armv7a: clang++ / gnustl NDK r14</li>
163<li>Android am64-v8a: clang++ / gnustl NDK r14</li>
164</ul>
165<dl class="section warning"><dt>Warning</dt><dd>Make sure to use a compatible toolchain to build your application or you will get some std::bad_alloc errors at runtime.</dd></dl>
166<h1><a class="anchor" id="S1_file_organisation"></a>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]167File organisation</h1>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]168<p>This archive contains:</p><ul>
169<li>The <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> header and source files</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]170<li>The latest Khronos OpenCL 1.2 C headers from the <a href="https://www.khronos.org/registry/cl/">Khronos OpenCL registry</a></li>
171<li>The latest Khronos cl2.hpp from the <a href="https://www.khronos.org/registry/cl/">Khronos OpenCL registry</a> (API version 2.1 when this document was written)</li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]172<li>The latest Khronos OpenGL ES 3.1 C headers from the <a href="https://www.khronos.org/registry/gles/">Khronos OpenGL ES registry</a></li>
173<li>The latest Khronos EGL 1.5 C headers from the <a href="https://www.khronos.org/registry/gles/">Khronos EGL registry</a></li>
174<li>The sources for a stub version of libOpenCL.so, libGLESv1_CM.so, libGLESv2.so and libEGL.so to help you build your application.</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]175<li>An examples folder containing a few examples to compile and link against the library.</li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]176<li>A <a class="el" href="dir_cbdb8362360e11eafe2fa3bc74cf0ffd.xhtml">utils</a> folder containing headers with some boiler plate code used by the examples.</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]177<li>This documentation.</li>
178</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]179<p>You should have the following file organisation: </p><pre class="fragment">.
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]180├── arm_compute --&gt; All the arm_compute headers
181│   ├── core
182│   │   ├── CL
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]183│   │   │   ├── CLKernelLibrary.h --&gt; Manages all the OpenCL kernels compilation and caching, provides accessors for the OpenCL Context.
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]184│   │   │   ├── CLKernels.h --&gt; Includes all the OpenCL kernels at once
185│   │   │   ├── CL specialisation of all the generic objects interfaces (ICLTensor, ICLImage, etc.)
186│   │   │   ├── kernels --&gt; Folder containing all the OpenCL kernels
187│   │   │   │   └── CL*Kernel.h
188│   │   │   └── OpenCL.h --&gt; Wrapper to configure the Khronos OpenCL C++ header
189│   │ ├── CPP
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]190│   │   │   ├── CPPKernels.h --&gt; Includes all the CPP kernels at once
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]191│   │ │   └── kernels --&gt; Folder containing all the CPP kernels
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]192│   │   │      └── CPP*Kernel.h
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]193│   │   ├── GLES_COMPUTE
194│   │   │   ├── GCKernelLibrary.h --&gt; Manages all the GLES kernels compilation and caching, provides accessors for the GLES Context.
195│   │   │   ├── GCKernels.h --&gt; Includes all the GLES kernels at once
196│   │   │   ├── GLES specialisation of all the generic objects interfaces (IGCTensor, IGCImage, etc.)
197│   │   │   ├── kernels --&gt; Folder containing all the GLES kernels
198│   │   │   │   └── GC*Kernel.h
199│   │   │   └── OpenGLES.h --&gt; Wrapper to configure the Khronos EGL and OpenGL ES C header
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]200│   │   ├── NEON
201│   │   │   ├── kernels --&gt; Folder containing all the NEON kernels
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]202│   │   │   │ ├── arm64 --&gt; Folder containing the interfaces for the assembly arm64 NEON kernels
203│   │   │   │ ├── arm32 --&gt; Folder containing the interfaces for the assembly arm32 NEON kernels
204│   │   │   │ ├── assembly --&gt; Folder containing the NEON assembly routines.
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]205│   │   │   │   └── NE*Kernel.h
206│   │   │   └── NEKernels.h --&gt; Includes all the NEON kernels at once
207│   │   ├── All common basic types (Types.h, Window, Coordinates, Iterator, etc.)
208│   │   ├── All generic objects interfaces (ITensor, IImage, etc.)
209│   │   └── Objects metadata classes (ImageInfo, TensorInfo, MultiImageInfo)
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]210│   ├── graph
211│   │   ├── CL --&gt; OpenCL specific operations
212│   │   │   └── CLMap.h / CLUnmap.h
213│   │   ├── nodes
214│   │   │   └── The various nodes supported by the graph API
215│   │   ├── Nodes.h --&gt; Includes all the Graph nodes at once.
216│   │   └── Graph objects ( INode, ITensorAccessor, Graph, etc.)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]217│   └── runtime
218│   ├── CL
219│   │   ├── CL objects &amp; allocators (CLArray, CLImage, CLTensor, etc.)
220│   │   ├── functions --&gt; Folder containing all the OpenCL functions
221│   │   │   └── CL*.h
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]222│   │   ├── CLScheduler.h --&gt; Interface to enqueue OpenCL kernels and get/set the OpenCL CommandQueue and ICLTuner.
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]223│   │   └── CLFunctions.h --&gt; Includes all the OpenCL functions at once
224│   ├── CPP
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]225│      │   ├── CPPKernels.h --&gt; Includes all the CPP functions at once.
226│   │   └── CPPScheduler.h --&gt; Basic pool of threads to execute CPP/NEON code on several cores in parallel
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]227│   ├── GLES_COMPUTE
228│   │   ├── GLES objects &amp; allocators (GCArray, GCImage, GCTensor, etc.)
229│   │   ├── functions --&gt; Folder containing all the GLES functions
230│   │   │   └── GC*.h
231│   │   ├── GCScheduler.h --&gt; Interface to enqueue GLES kernels and get/set the GLES CommandQueue.
232│   │   └── GCFunctions.h --&gt; Includes all the GLES functions at once
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]233│   ├── NEON
234│   │ ├── functions --&gt; Folder containing all the NEON functions
235│   │ │   └── NE*.h
236│   │ └── NEFunctions.h --&gt; Includes all the NEON functions at once
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]237│   ├── OMP
238│   │   └── OMPScheduler.h --&gt; OpenMP scheduler (Alternative to the CPPScheduler)
239│ ├── Memory manager files (LifetimeManager, PoolManager, etc.)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]240│   └── Basic implementations of the generic object interfaces (Array, Image, Tensor, etc.)
241├── documentation
242│   ├── index.xhtml
243│   └── ...
244├── documentation.xhtml -&gt; documentation/index.xhtml
245├── examples
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]246│   ├── cl_*.cpp --&gt; OpenCL examples
247│   ├── gc_*.cpp --&gt; GLES compute shaders examples
248│   ├── graph_*.cpp --&gt; Graph examples
249│   ├── neoncl_*.cpp --&gt; NEON / OpenCL interoperability examples
250│   └── neon_*.cpp --&gt; NEON examples
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]251├── include
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]252│   ├── CL
253│   │ └── Khronos OpenCL C headers and C++ wrapper
254│   ├── half --&gt; FP16 library available from http://half.sourceforge.net
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]255│   ├── libnpy --&gt; Library to load / write npy buffers, available from https://github.com/llohse/libnpy
256│  └── linux --&gt; Headers only needed for Linux builds
257│   └── Khronos EGL and OpenGLES headers
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]258├── opencl-1.2-stubs
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]259│ └── opencl_stubs.c --&gt; OpenCL stubs implementation
260├── opengles-3.1-stubs
261│   ├── EGL.c --&gt; EGL stubs implementation
262│   └── GLESv2.c --&gt; GLESv2 stubs implementation
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]263├── scripts
264│   ├── caffe_data_extractor.py --&gt; Basic script to export weights from Caffe to npy files
265│   └── tensorflow_data_extractor.py --&gt; Basic script to export weights from Tensor Flow to npy files
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]266├── src
267│   ├── core
268│ │ └── ... (Same structure as headers)
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]269│   │ ├── CL
270│   │ │ └── cl_kernels --&gt; All the OpenCL kernels
271│   │ └── GLES_COMPUTE
272│   │ └── cs_shaders --&gt; All the OpenGL ES Compute Shaders
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]273│   ├── graph
274│ │ └── ... (Same structure as headers)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]275│ └── runtime
276│ └── ... (Same structure as headers)
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]277├── support
278│ └── Various headers to work around toolchains / platform issues.
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]279├── tests
280│   ├── All test related files shared between validation and benchmark
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]281│   ├── CL --&gt; OpenCL accessors
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]282│   ├── GLES_COMPUTE --&gt; GLES accessors
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]283│   ├── NEON --&gt; NEON accessors
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]284│   ├── benchmark --&gt; Sources for benchmarking
285│ │ ├── Benchmark specific files
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]286│ │ ├── CL --&gt; OpenCL benchmarking tests
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]287│ │ ├── GLES_COMPUTE --&gt; GLES benchmarking tests
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]288│ │ └── NEON --&gt; NEON benchmarking tests
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]289│   ├── datasets
290│ │ └── Datasets for all the validation / benchmark tests, layer configurations for various networks, etc.
291│   ├── framework
292│ │ └── Boiler plate code for both validation and benchmark test suites (Command line parsers, instruments, output loggers, etc.)
293│   ├── networks
294│ │ └── Examples of how to instantiate networks.
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]295│   ├── validation --&gt; Sources for validation
296│ │ ├── Validation specific files
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]297│ │ ├── CL --&gt; OpenCL validation tests
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]298│ │ ├── GLES_COMPUTE --&gt; GLES validation tests
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]299│ │ ├── CPP --&gt; C++ reference implementations
300│   │ ├── fixtures
301│ │ │ └── Fixtures to initialise and run the runtime Functions.
302│ │ └── NEON --&gt; NEON validation tests
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]303│   └── dataset --&gt; Datasets defining common sets of input parameters
304└── utils --&gt; Boiler plate code used by examples
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]305 └── Various utilities to print types, load / store assets, etc.
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]306</pre><h1><a class="anchor" id="S2_versions_changelog"></a>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]307Release versions and changelog</h1>
308<h2><a class="anchor" id="S2_1_versions"></a>
309Release versions</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]310<p>All releases are numbered vYY.MM Where YY are the last two digits of the year, and MM the month number. If there is more than one release in a month then an extra sequential number is appended at the end: </p><pre class="fragment">v17.03 (First release of March 2017)
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]311v17.03.1 (Second release of March 2017)
312v17.04 (First release of April 2017)
313</pre><dl class="section note"><dt>Note</dt><dd>We're aiming at releasing one major public release with new features per quarter. All releases in between will only contain bug fixes.</dd></dl>
314<h2><a class="anchor" id="S2_2_changelog"></a>
315Changelog</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]316<p>v17.12 Public major release</p><ul>
317<li>Most machine learning functions on OpenCL support the new data type QASYMM8</li>
318<li>Introduced logging interface</li>
319<li>Introduced opencl timer</li>
320<li>Reworked GEMMLowp interface</li>
321<li>Added new NEON assembly kernels for GEMMLowp, SGEMM and HGEMM</li>
322<li>Added validation method for most Machine Learning kernels / functions</li>
323<li>Added new graph examples such as googlenet, mobilenet, squeezenet, vgg16 and vgg19</li>
324<li>Added sgemm example for OpenCL</li>
325<li>Added absolute difference example for GLES compute</li>
326<li>Added new tests and benchmarks in validation and benchmark frameworks</li>
327<li>Added new kernels / functions for GLES compute</li>
328<li>New OpenGL ES kernels / functions<ul>
329<li><a class="el" href="classarm__compute_1_1_g_c_absolute_difference_kernel.xhtml">arm_compute::GCAbsoluteDifferenceKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_absolute_difference.xhtml">arm_compute::GCAbsoluteDifference</a></li>
330<li><a class="el" href="classarm__compute_1_1_g_c_activation_layer_kernel.xhtml">arm_compute::GCActivationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_activation_layer.xhtml">arm_compute::GCActivationLayer</a></li>
331<li><a class="el" href="classarm__compute_1_1_g_c_batch_normalization_layer_kernel.xhtml">arm_compute::GCBatchNormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_batch_normalization_layer.xhtml">arm_compute::GCBatchNormalizationLayer</a></li>
332<li><a class="el" href="classarm__compute_1_1_g_c_col2_im_kernel.xhtml">arm_compute::GCCol2ImKernel</a></li>
333<li><a class="el" href="classarm__compute_1_1_g_c_depth_concatenate_layer_kernel.xhtml">arm_compute::GCDepthConcatenateLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_depth_concatenate_layer.xhtml">arm_compute::GCDepthConcatenateLayer</a></li>
334<li><a class="el" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml">arm_compute::GCDirectConvolutionLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_direct_convolution_layer.xhtml">arm_compute::GCDirectConvolutionLayer</a></li>
335<li><a class="el" href="classarm__compute_1_1_g_c_dropout_layer_kernel.xhtml">arm_compute::GCDropoutLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_dropout_layer.xhtml">arm_compute::GCDropoutLayer</a></li>
336<li><a class="el" href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml">arm_compute::GCFillBorderKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_fill_border.xhtml">arm_compute::GCFillBorder</a></li>
337<li><a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_interleave4x4_kernel.xhtml">arm_compute::GCGEMMInterleave4x4Kernel</a> / <a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_interleave4x4.xhtml">arm_compute::GCGEMMInterleave4x4</a></li>
338<li><a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_accumulate_biases_kernel.xhtml">arm_compute::GCGEMMMatrixAccumulateBiasesKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_addition_kernel.xhtml">arm_compute::GCGEMMMatrixAdditionKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_multiply_kernel.xhtml">arm_compute::GCGEMMMatrixMultiplyKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_g_e_m_m.xhtml">arm_compute::GCGEMM</a></li>
339<li><a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_transpose1x_w_kernel.xhtml">arm_compute::GCGEMMTranspose1xWKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_transpose1x_w.xhtml">arm_compute::GCGEMMTranspose1xW</a></li>
340<li><a class="el" href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml">arm_compute::GCIm2ColKernel</a></li>
341<li><a class="el" href="classarm__compute_1_1_g_c_normalization_layer_kernel.xhtml">arm_compute::GCNormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_normalization_layer.xhtml">arm_compute::GCNormalizationLayer</a></li>
342<li><a class="el" href="classarm__compute_1_1_g_c_pixel_wise_multiplication_kernel.xhtml">arm_compute::GCPixelWiseMultiplicationKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_pixel_wise_multiplication.xhtml">arm_compute::GCPixelWiseMultiplication</a></li>
343<li><a class="el" href="classarm__compute_1_1_g_c_pooling_layer_kernel.xhtml">arm_compute::GCPoolingLayerKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_pooling_layer.xhtml">arm_compute::GCPoolingLayer</a></li>
344<li><a class="el" href="classarm__compute_1_1_g_c_logits1_d_max_kernel.xhtml">arm_compute::GCLogits1DMaxKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_logits1_d_shift_exp_sum_kernel.xhtml">arm_compute::GCLogits1DShiftExpSumKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_logits1_d_norm_kernel.xhtml">arm_compute::GCLogits1DNormKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_softmax_layer.xhtml">arm_compute::GCSoftmaxLayer</a></li>
345<li><a class="el" href="classarm__compute_1_1_g_c_transpose_kernel.xhtml">arm_compute::GCTransposeKernel</a> / <a class="el" href="classarm__compute_1_1_g_c_transpose.xhtml">arm_compute::GCTranspose</a></li>
346</ul>
347</li>
348<li>New NEON kernels / functions<ul>
349<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_a_arch64_a53_kernel.xhtml">arm_compute::NEGEMMLowpAArch64A53Kernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_a_arch64_kernel.xhtml">arm_compute::NEGEMMLowpAArch64Kernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_a_arch64_v8_p4_kernel.xhtml">arm_compute::NEGEMMLowpAArch64V8P4Kernel</a> / arm_compute::NEGEMMInterleavedBlockedKernel / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_assembly_matrix_multiply_core.xhtml">arm_compute::NEGEMMLowpAssemblyMatrixMultiplyCore</a></li>
350<li><a class="el" href="classarm__compute_1_1_n_e_h_g_e_m_m_a_arch64_f_p16_kernel.xhtml">arm_compute::NEHGEMMAArch64FP16Kernel</a></li>
351<li><a class="el" href="classarm__compute_1_1_n_e_depthwise_convolution_layer3x3_kernel.xhtml">arm_compute::NEDepthwiseConvolutionLayer3x3Kernel</a> / <a class="el" href="classarm__compute_1_1_n_e_depthwise_im2_col_kernel.xhtml">arm_compute::NEDepthwiseIm2ColKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_vector_multiply_kernel.xhtml">arm_compute::NEGEMMMatrixVectorMultiplyKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_depthwise_vector_to_tensor_kernel.xhtml">arm_compute::NEDepthwiseVectorToTensorKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_depthwise_convolution_layer.xhtml">arm_compute::NEDepthwiseConvolutionLayer</a></li>
352<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_offset_contribution_kernel.xhtml">arm_compute::NEGEMMLowpOffsetContributionKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_a_reduction_kernel.xhtml">arm_compute::NEGEMMLowpMatrixAReductionKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_b_reduction_kernel.xhtml">arm_compute::NEGEMMLowpMatrixBReductionKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml">arm_compute::NEGEMMLowpMatrixMultiplyCore</a></li>
353<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point_kernel.xhtml">arm_compute::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">arm_compute::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</a></li>
354<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_kernel.xhtml">arm_compute::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale.xhtml">arm_compute::NEGEMMLowpQuantizeDownInt32ToUint8Scale</a></li>
355<li><a class="el" href="classarm__compute_1_1_n_e_winograd_layer_kernel.xhtml">arm_compute::NEWinogradLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_winograd_layer.xhtml">arm_compute::NEWinogradLayer</a></li>
356</ul>
357</li>
358<li>New OpenCL kernels / functions<ul>
359<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_offset_contribution_kernel.xhtml">arm_compute::CLGEMMLowpOffsetContributionKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_a_reduction_kernel.xhtml">arm_compute::CLGEMMLowpMatrixAReductionKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_b_reduction_kernel.xhtml">arm_compute::CLGEMMLowpMatrixBReductionKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml">arm_compute::CLGEMMLowpMatrixMultiplyCore</a></li>
360<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point_kernel.xhtml">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</a></li>
361<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_kernel.xhtml">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale.xhtml">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8Scale</a></li>
362</ul>
363</li>
364<li>New graph nodes for NEON and OpenCL<ul>
365<li><a class="el" href="classarm__compute_1_1graph_1_1_branch_layer.xhtml">arm_compute::graph::BranchLayer</a></li>
366<li><a class="el" href="classarm__compute_1_1graph_1_1_depth_convert_layer.xhtml">arm_compute::graph::DepthConvertLayer</a></li>
367<li><a class="el" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer.xhtml">arm_compute::graph::DepthwiseConvolutionLayer</a></li>
368<li><a class="el" href="classarm__compute_1_1graph_1_1_dequantization_layer.xhtml">arm_compute::graph::DequantizationLayer</a></li>
369<li><a class="el" href="classarm__compute_1_1graph_1_1_flatten_layer.xhtml">arm_compute::graph::FlattenLayer</a></li>
370<li><a class="el" href="classarm__compute_1_1graph_1_1_quantization_layer.xhtml">arm_compute::graph::QuantizationLayer</a></li>
371<li><a class="el" href="classarm__compute_1_1graph_1_1_reshape_layer.xhtml">arm_compute::graph::ReshapeLayer</a></li>
372</ul>
373</li>
374</ul>
375<p>v17.10 Public maintenance release</p><ul>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]376<li>Bug fixes:<ul>
377<li>Check the maximum local workgroup size supported by OpenCL devices</li>
378<li>Minor documentation updates (Fixed instructions to build the examples)</li>
379<li>Introduced a <a class="el" href="classarm__compute_1_1graph_1_1_graph_context.xhtml" title="Graph context. ">arm_compute::graph::GraphContext</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]380<li>Added a few new Graph nodes, support for branches and grouping.</li>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]381<li>Automatically enable cl_printf in debug builds</li>
382<li>Fixed bare metal builds for armv7a</li>
383<li>Added AlexNet and cartoon effect examples</li>
384<li>Fixed library builds: libraries are no longer built as supersets of each other.(It means application using the Runtime part of the library now need to link against both libarm_compute_core and libarm_compute)</li>
385</ul>
386</li>
387</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]388<p>v17.09 Public major release</p><ul>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]389<li>Experimental Graph support: initial implementation of a simple stream API to easily chain machine learning layers.</li>
390<li>Memory Manager (<a class="el" href="classarm__compute_1_1_blob_lifetime_manager.xhtml">arm_compute::BlobLifetimeManager</a>, <a class="el" href="classarm__compute_1_1_blob_memory_pool.xhtml">arm_compute::BlobMemoryPool</a>, <a class="el" href="classarm__compute_1_1_i_lifetime_manager.xhtml">arm_compute::ILifetimeManager</a>, <a class="el" href="classarm__compute_1_1_i_memory_group.xhtml">arm_compute::IMemoryGroup</a>, <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">arm_compute::IMemoryManager</a>, <a class="el" href="classarm__compute_1_1_i_memory_pool.xhtml">arm_compute::IMemoryPool</a>, <a class="el" href="classarm__compute_1_1_i_pool_manager.xhtml">arm_compute::IPoolManager</a>, <a class="el" href="classarm__compute_1_1_memory_manager_on_demand.xhtml">arm_compute::MemoryManagerOnDemand</a>, <a class="el" href="classarm__compute_1_1_pool_manager.xhtml">arm_compute::PoolManager</a>)</li>
391<li>New validation and benchmark frameworks (Boost and Google frameworks replaced by homemade framework).</li>
392<li>Most machine learning functions support both fixed point 8 and 16 bit (QS8, QS16) for both NEON and OpenCL.</li>
393<li>New NEON kernels / functions:<ul>
394<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_base_kernel.xhtml">arm_compute::NEGEMMAssemblyBaseKernel</a> <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_a_arch64_kernel.xhtml">arm_compute::NEGEMMAArch64Kernel</a></li>
395<li><a class="el" href="classarm__compute_1_1_n_e_dequantization_layer_kernel.xhtml">arm_compute::NEDequantizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_dequantization_layer.xhtml">arm_compute::NEDequantizationLayer</a></li>
396<li><a class="el" href="classarm__compute_1_1_n_e_floor_kernel.xhtml">arm_compute::NEFloorKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_floor.xhtml">arm_compute::NEFloor</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]397<li><a class="el" href="classarm__compute_1_1_n_e_l2_normalize_layer_kernel.xhtml">arm_compute::NEL2NormalizeLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_l2_normalize_layer.xhtml">arm_compute::NEL2NormalizeLayer</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]398<li><a class="el" href="classarm__compute_1_1_n_e_quantization_layer_kernel.xhtml">arm_compute::NEQuantizationLayerKernel</a> <a class="el" href="classarm__compute_1_1_n_e_min_max_layer_kernel.xhtml">arm_compute::NEMinMaxLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_quantization_layer.xhtml">arm_compute::NEQuantizationLayer</a></li>
399<li><a class="el" href="classarm__compute_1_1_n_e_r_o_i_pooling_layer_kernel.xhtml">arm_compute::NEROIPoolingLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_r_o_i_pooling_layer.xhtml">arm_compute::NEROIPoolingLayer</a></li>
400<li><a class="el" href="classarm__compute_1_1_n_e_reduction_operation_kernel.xhtml">arm_compute::NEReductionOperationKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_reduction_operation.xhtml">arm_compute::NEReductionOperation</a></li>
401<li><a class="el" href="classarm__compute_1_1_n_e_reshape_layer_kernel.xhtml">arm_compute::NEReshapeLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_reshape_layer.xhtml">arm_compute::NEReshapeLayer</a></li>
402</ul>
403</li>
404<li>New OpenCL kernels / functions:<ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]405<li><a class="el" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_kernel.xhtml">arm_compute::CLDepthwiseConvolutionLayer3x3Kernel</a> <a class="el" href="classarm__compute_1_1_c_l_depthwise_im2_col_kernel.xhtml">arm_compute::CLDepthwiseIm2ColKernel</a> <a class="el" href="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel.xhtml">arm_compute::CLDepthwiseVectorToTensorKernel</a> <a class="el" href="classarm__compute_1_1_c_l_depthwise_weights_reshape_kernel.xhtml">arm_compute::CLDepthwiseWeightsReshapeKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml">arm_compute::CLDepthwiseConvolutionLayer3x3</a> <a class="el" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml">arm_compute::CLDepthwiseConvolutionLayer</a> <a class="el" href="classarm__compute_1_1_c_l_depthwise_separable_convolution_layer.xhtml">arm_compute::CLDepthwiseSeparableConvolutionLayer</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]406<li><a class="el" href="classarm__compute_1_1_c_l_dequantization_layer_kernel.xhtml">arm_compute::CLDequantizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_dequantization_layer.xhtml">arm_compute::CLDequantizationLayer</a></li>
407<li><a class="el" href="classarm__compute_1_1_c_l_direct_convolution_layer_kernel.xhtml">arm_compute::CLDirectConvolutionLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_direct_convolution_layer.xhtml">arm_compute::CLDirectConvolutionLayer</a></li>
408<li><a class="el" href="classarm__compute_1_1_c_l_flatten_layer.xhtml">arm_compute::CLFlattenLayer</a></li>
409<li><a class="el" href="classarm__compute_1_1_c_l_floor_kernel.xhtml">arm_compute::CLFloorKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_floor.xhtml">arm_compute::CLFloor</a></li>
410<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_transpose1x_w.xhtml">arm_compute::CLGEMMTranspose1xW</a></li>
411<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml">arm_compute::CLGEMMMatrixVectorMultiplyKernel</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]412<li><a class="el" href="classarm__compute_1_1_c_l_l2_normalize_layer_kernel.xhtml">arm_compute::CLL2NormalizeLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_l2_normalize_layer.xhtml">arm_compute::CLL2NormalizeLayer</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]413<li><a class="el" href="classarm__compute_1_1_c_l_quantization_layer_kernel.xhtml">arm_compute::CLQuantizationLayerKernel</a> <a class="el" href="classarm__compute_1_1_c_l_min_max_layer_kernel.xhtml">arm_compute::CLMinMaxLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_quantization_layer.xhtml">arm_compute::CLQuantizationLayer</a></li>
414<li><a class="el" href="classarm__compute_1_1_c_l_r_o_i_pooling_layer_kernel.xhtml">arm_compute::CLROIPoolingLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_r_o_i_pooling_layer.xhtml">arm_compute::CLROIPoolingLayer</a></li>
415<li><a class="el" href="classarm__compute_1_1_c_l_reduction_operation_kernel.xhtml">arm_compute::CLReductionOperationKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_reduction_operation.xhtml">arm_compute::CLReductionOperation</a></li>
416<li><a class="el" href="classarm__compute_1_1_c_l_reshape_layer_kernel.xhtml">arm_compute::CLReshapeLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_reshape_layer.xhtml">arm_compute::CLReshapeLayer</a></li>
417</ul>
418</li>
419</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]420<p>v17.06 Public major release</p><ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]421<li>Various bug fixes</li>
422<li>Added support for fixed point 8 bit (QS8) to the various NEON machine learning kernels.</li>
423<li>Added unit tests and benchmarks (AlexNet, LeNet)</li>
424<li>Added support for sub tensors.</li>
425<li>Added infrastructure to provide GPU specific optimisation for some OpenCL kernels.</li>
426<li>Added <a class="el" href="classarm__compute_1_1_o_m_p_scheduler.xhtml">arm_compute::OMPScheduler</a> (OpenMP) scheduler for NEON</li>
427<li>Added <a class="el" href="classarm__compute_1_1_single_thread_scheduler.xhtml">arm_compute::SingleThreadScheduler</a> scheduler for NEON (For bare metal)</li>
428<li>User can specify his own scheduler by implementing the <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml">arm_compute::IScheduler</a> interface.</li>
429<li>New OpenCL kernels / functions:<ul>
430<li><a class="el" href="classarm__compute_1_1_c_l_batch_normalization_layer_kernel.xhtml">arm_compute::CLBatchNormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_batch_normalization_layer.xhtml">arm_compute::CLBatchNormalizationLayer</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]431<li><a class="el" href="classarm__compute_1_1_c_l_depth_concatenate_layer_kernel.xhtml">arm_compute::CLDepthConcatenateLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_depth_concatenate_layer.xhtml">arm_compute::CLDepthConcatenateLayer</a></li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]432<li><a class="el" href="classarm__compute_1_1_c_l_h_o_g_orientation_binning_kernel.xhtml">arm_compute::CLHOGOrientationBinningKernel</a> <a class="el" href="classarm__compute_1_1_c_l_h_o_g_block_normalization_kernel.xhtml">arm_compute::CLHOGBlockNormalizationKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_h_o_g_detector_kernel.xhtml">arm_compute::CLHOGDetectorKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_h_o_g_descriptor.xhtml">arm_compute::CLHOGDescriptor</a> <a class="el" href="classarm__compute_1_1_c_l_h_o_g_detector.xhtml">arm_compute::CLHOGDetector</a> <a class="el" href="classarm__compute_1_1_c_l_h_o_g_gradient.xhtml">arm_compute::CLHOGGradient</a> <a class="el" href="classarm__compute_1_1_c_l_h_o_g_multi_detection.xhtml">arm_compute::CLHOGMultiDetection</a></li>
433<li><a class="el" href="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel.xhtml">arm_compute::CLLocallyConnectedMatrixMultiplyKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml">arm_compute::CLLocallyConnectedLayer</a></li>
434<li><a class="el" href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml">arm_compute::CLWeightsReshapeKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml">arm_compute::CLConvolutionLayerReshapeWeights</a></li>
435</ul>
436</li>
437<li>New C++ kernels:<ul>
438<li><a class="el" href="classarm__compute_1_1_c_p_p_detection_window_non_maxima_suppression_kernel.xhtml">arm_compute::CPPDetectionWindowNonMaximaSuppressionKernel</a></li>
439</ul>
440</li>
441<li>New NEON kernels / functions:<ul>
442<li><a class="el" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml">arm_compute::NEBatchNormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_batch_normalization_layer.xhtml">arm_compute::NEBatchNormalizationLayer</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]443<li><a class="el" href="classarm__compute_1_1_n_e_depth_concatenate_layer_kernel.xhtml">arm_compute::NEDepthConcatenateLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_depth_concatenate_layer.xhtml">arm_compute::NEDepthConcatenateLayer</a></li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]444<li><a class="el" href="classarm__compute_1_1_n_e_direct_convolution_layer_kernel.xhtml">arm_compute::NEDirectConvolutionLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_direct_convolution_layer.xhtml">arm_compute::NEDirectConvolutionLayer</a></li>
445<li><a class="el" href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml">arm_compute::NELocallyConnectedMatrixMultiplyKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">arm_compute::NELocallyConnectedLayer</a></li>
446<li><a class="el" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml">arm_compute::NEWeightsReshapeKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml">arm_compute::NEConvolutionLayerReshapeWeights</a></li>
447</ul>
448</li>
449</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]450<p>v17.05 Public bug fixes release</p><ul>
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]451<li>Various bug fixes</li>
452<li>Remaining of the functions ported to use accurate padding.</li>
453<li>Library does not link against OpenCL anymore (It uses dlopen / dlsym at runtime instead to determine whether or not OpenCL is available).</li>
454<li>Added "free" method to allocator.</li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]455<li>Minimum version of g++ required for armv7 Linux changed from 4.8 to 4.9</li>
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]456</ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]457<p>v17.04 Public bug fixes release</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]458<p>The following functions have been ported to use the new accurate padding:</p><ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]459<li><a class="el" href="classarm__compute_1_1_c_l_color_convert_kernel.xhtml">arm_compute::CLColorConvertKernel</a></li>
460<li><a class="el" href="classarm__compute_1_1_c_l_edge_non_max_suppression_kernel.xhtml">arm_compute::CLEdgeNonMaxSuppressionKernel</a></li>
461<li><a class="el" href="classarm__compute_1_1_c_l_edge_trace_kernel.xhtml">arm_compute::CLEdgeTraceKernel</a></li>
462<li><a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_hor_kernel.xhtml">arm_compute::CLGaussianPyramidHorKernel</a></li>
463<li><a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_vert_kernel.xhtml">arm_compute::CLGaussianPyramidVertKernel</a></li>
464<li><a class="el" href="classarm__compute_1_1_c_l_gradient_kernel.xhtml">arm_compute::CLGradientKernel</a></li>
465<li><a class="el" href="classarm__compute_1_1_n_e_channel_combine_kernel.xhtml">arm_compute::NEChannelCombineKernel</a></li>
466<li><a class="el" href="classarm__compute_1_1_n_e_fill_array_kernel.xhtml">arm_compute::NEFillArrayKernel</a></li>
467<li><a class="el" href="classarm__compute_1_1_n_e_gaussian_pyramid_hor_kernel.xhtml">arm_compute::NEGaussianPyramidHorKernel</a></li>
468<li><a class="el" href="classarm__compute_1_1_n_e_gaussian_pyramid_vert_kernel.xhtml">arm_compute::NEGaussianPyramidVertKernel</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]469<li><a class="el" href="namespacearm__compute.xhtml#a0b6679b5d5c7f7dc527258181b04cf35">arm_compute::NEHarrisScoreFP16Kernel</a></li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]470<li><a class="el" href="classarm__compute_1_1_n_e_harris_score_kernel.xhtml">arm_compute::NEHarrisScoreKernel</a></li>
471<li><a class="el" href="classarm__compute_1_1_n_e_h_o_g_detector_kernel.xhtml">arm_compute::NEHOGDetectorKernel</a></li>
472<li><a class="el" href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml">arm_compute::NELogits1DMaxKernel</a></li>
473<li><a class="el" href="classarm__compute_1_1_n_e_logits1_d_shift_exp_sum_kernel.xhtml">arm_compute::NELogits1DShiftExpSumKernel</a></li>
474<li><a class="el" href="classarm__compute_1_1_n_e_logits1_d_norm_kernel.xhtml">arm_compute::NELogits1DNormKernel</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]475<li><a class="el" href="namespacearm__compute.xhtml#a38cad49e6beaef76bc1ec5064c9e9dba">arm_compute::NENonMaximaSuppression3x3FP16Kernel</a></li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]476<li><a class="el" href="classarm__compute_1_1_n_e_non_maxima_suppression3x3_kernel.xhtml">arm_compute::NENonMaximaSuppression3x3Kernel</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]477</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]478<p>v17.03.1 First Major public release of the sources</p><ul>
479<li>Renamed the library to <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]480<li>New CPP target introduced for C++ kernels shared between NEON and CL functions.</li>
481<li>New padding calculation interface introduced and ported most kernels / functions to use it.</li>
482<li>New OpenCL kernels / functions:<ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]483<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_kernel.xhtml">arm_compute::CLGEMMLowpMatrixMultiplyKernel</a> / arm_compute::CLGEMMLowp</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]484</ul>
485</li>
486<li>New NEON kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]487<li><a class="el" href="classarm__compute_1_1_n_e_normalization_layer_kernel.xhtml">arm_compute::NENormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_normalization_layer.xhtml">arm_compute::NENormalizationLayer</a></li>
488<li><a class="el" href="classarm__compute_1_1_n_e_transpose_kernel.xhtml">arm_compute::NETransposeKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_transpose.xhtml">arm_compute::NETranspose</a></li>
489<li><a class="el" href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml">arm_compute::NELogits1DMaxKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_logits1_d_shift_exp_sum_kernel.xhtml">arm_compute::NELogits1DShiftExpSumKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_logits1_d_norm_kernel.xhtml">arm_compute::NELogits1DNormKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">arm_compute::NESoftmaxLayer</a></li>
490<li><a class="el" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml">arm_compute::NEIm2ColKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml">arm_compute::NECol2ImKernel</a>, arm_compute::NEConvolutionLayerWeightsReshapeKernel / <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">arm_compute::NEConvolutionLayer</a></li>
491<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml">arm_compute::NEGEMMMatrixAccumulateBiasesKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">arm_compute::NEFullyConnectedLayer</a></li>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]492<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_kernel.xhtml">arm_compute::NEGEMMLowpMatrixMultiplyKernel</a> / arm_compute::NEGEMMLowp</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]493</ul>
494</li>
495</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]496<p>v17.03 Sources preview</p><ul>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]497<li>New OpenCL kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]498<li><a class="el" href="classarm__compute_1_1_c_l_gradient_kernel.xhtml">arm_compute::CLGradientKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_edge_non_max_suppression_kernel.xhtml">arm_compute::CLEdgeNonMaxSuppressionKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_edge_trace_kernel.xhtml">arm_compute::CLEdgeTraceKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_canny_edge.xhtml">arm_compute::CLCannyEdge</a></li>
499<li>GEMM refactoring + FP16 support: <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_interleave4x4_kernel.xhtml">arm_compute::CLGEMMInterleave4x4Kernel</a>, <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_transpose1x_w_kernel.xhtml">arm_compute::CLGEMMTranspose1xWKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_multiply_kernel.xhtml">arm_compute::CLGEMMMatrixMultiplyKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_addition_kernel.xhtml">arm_compute::CLGEMMMatrixAdditionKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml">arm_compute::CLGEMM</a></li>
500<li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml">arm_compute::CLGEMMMatrixAccumulateBiasesKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">arm_compute::CLFullyConnectedLayer</a></li>
501<li><a class="el" href="classarm__compute_1_1_c_l_transpose_kernel.xhtml">arm_compute::CLTransposeKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_transpose.xhtml">arm_compute::CLTranspose</a></li>
502<li><a class="el" href="classarm__compute_1_1_c_l_l_k_tracker_init_kernel.xhtml">arm_compute::CLLKTrackerInitKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_l_k_tracker_stage0_kernel.xhtml">arm_compute::CLLKTrackerStage0Kernel</a>, <a class="el" href="classarm__compute_1_1_c_l_l_k_tracker_stage1_kernel.xhtml">arm_compute::CLLKTrackerStage1Kernel</a>, <a class="el" href="classarm__compute_1_1_c_l_l_k_tracker_finalize_kernel.xhtml">arm_compute::CLLKTrackerFinalizeKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_optical_flow.xhtml">arm_compute::CLOpticalFlow</a></li>
503<li><a class="el" href="classarm__compute_1_1_c_l_normalization_layer_kernel.xhtml">arm_compute::CLNormalizationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_normalization_layer.xhtml">arm_compute::CLNormalizationLayer</a></li>
504<li><a class="el" href="classarm__compute_1_1_c_l_laplacian_pyramid.xhtml">arm_compute::CLLaplacianPyramid</a>, <a class="el" href="classarm__compute_1_1_c_l_laplacian_reconstruct.xhtml">arm_compute::CLLaplacianReconstruct</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]505</ul>
506</li>
507<li>New NEON kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]508<li><a class="el" href="classarm__compute_1_1_n_e_activation_layer_kernel.xhtml">arm_compute::NEActivationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">arm_compute::NEActivationLayer</a></li>
509<li>GEMM refactoring + FP16 support (Requires armv8.2 CPU): <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleave4x4_kernel.xhtml">arm_compute::NEGEMMInterleave4x4Kernel</a>, <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml">arm_compute::NEGEMMTranspose1xWKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml">arm_compute::NEGEMMMatrixMultiplyKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_addition_kernel.xhtml">arm_compute::NEGEMMMatrixAdditionKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">arm_compute::NEGEMM</a></li>
510<li><a class="el" href="classarm__compute_1_1_n_e_pooling_layer_kernel.xhtml">arm_compute::NEPoolingLayerKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">arm_compute::NEPoolingLayer</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]511</ul>
512</li>
513</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]514<p>v17.02.1 Sources preview</p><ul>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]515<li>New OpenCL kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]516<li><a class="el" href="classarm__compute_1_1_c_l_logits1_d_max_kernel.xhtml">arm_compute::CLLogits1DMaxKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_logits1_d_shift_exp_sum_kernel.xhtml">arm_compute::CLLogits1DShiftExpSumKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_logits1_d_norm_kernel.xhtml">arm_compute::CLLogits1DNormKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_softmax_layer.xhtml">arm_compute::CLSoftmaxLayer</a></li>
517<li><a class="el" href="classarm__compute_1_1_c_l_pooling_layer_kernel.xhtml">arm_compute::CLPoolingLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">arm_compute::CLPoolingLayer</a></li>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]518<li><a class="el" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml">arm_compute::CLIm2ColKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml">arm_compute::CLCol2ImKernel</a>, arm_compute::CLConvolutionLayerWeightsReshapeKernel / <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">arm_compute::CLConvolutionLayer</a></li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]519<li><a class="el" href="classarm__compute_1_1_c_l_remap_kernel.xhtml">arm_compute::CLRemapKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_remap.xhtml">arm_compute::CLRemap</a></li>
520<li><a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_hor_kernel.xhtml">arm_compute::CLGaussianPyramidHorKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_vert_kernel.xhtml">arm_compute::CLGaussianPyramidVertKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid.xhtml">arm_compute::CLGaussianPyramid</a>, <a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_half.xhtml">arm_compute::CLGaussianPyramidHalf</a>, <a class="el" href="classarm__compute_1_1_c_l_gaussian_pyramid_orb.xhtml">arm_compute::CLGaussianPyramidOrb</a></li>
521<li><a class="el" href="classarm__compute_1_1_c_l_min_max_kernel.xhtml">arm_compute::CLMinMaxKernel</a>, <a class="el" href="classarm__compute_1_1_c_l_min_max_location_kernel.xhtml">arm_compute::CLMinMaxLocationKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_min_max_location.xhtml">arm_compute::CLMinMaxLocation</a></li>
522<li><a class="el" href="classarm__compute_1_1_c_l_non_linear_filter_kernel.xhtml">arm_compute::CLNonLinearFilterKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_non_linear_filter.xhtml">arm_compute::CLNonLinearFilter</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]523</ul>
524</li>
525<li>New NEON FP16 kernels (Requires armv8.2 CPU)<ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]526<li><a class="el" href="namespacearm__compute.xhtml#aff99c045b07329b332b1cb97a2dd1518">arm_compute::NEAccumulateWeightedFP16Kernel</a></li>
527<li><a class="el" href="namespacearm__compute.xhtml#a96f7f6f98dc47e0dc3b928bf87397ebf">arm_compute::NEBox3x3FP16Kernel</a></li>
528<li><a class="el" href="namespacearm__compute.xhtml#a38cad49e6beaef76bc1ec5064c9e9dba">arm_compute::NENonMaximaSuppression3x3FP16Kernel</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]529</ul>
530</li>
531</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]532<p>v17.02 Sources preview</p><ul>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]533<li>New OpenCL kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]534<li><a class="el" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml">arm_compute::CLActivationLayerKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">arm_compute::CLActivationLayer</a></li>
535<li><a class="el" href="classarm__compute_1_1_c_l_channel_combine_kernel.xhtml">arm_compute::CLChannelCombineKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_channel_combine.xhtml">arm_compute::CLChannelCombine</a></li>
536<li><a class="el" href="classarm__compute_1_1_c_l_derivative_kernel.xhtml">arm_compute::CLDerivativeKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_channel_extract.xhtml">arm_compute::CLChannelExtract</a></li>
537<li><a class="el" href="classarm__compute_1_1_c_l_fast_corners_kernel.xhtml">arm_compute::CLFastCornersKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_fast_corners.xhtml">arm_compute::CLFastCorners</a></li>
538<li><a class="el" href="classarm__compute_1_1_c_l_mean_std_dev_kernel.xhtml">arm_compute::CLMeanStdDevKernel</a> / <a class="el" href="classarm__compute_1_1_c_l_mean_std_dev.xhtml">arm_compute::CLMeanStdDev</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]539</ul>
540</li>
541<li>New NEON kernels / functions:<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]542<li>HOG / SVM: <a class="el" href="classarm__compute_1_1_n_e_h_o_g_orientation_binning_kernel.xhtml">arm_compute::NEHOGOrientationBinningKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_h_o_g_block_normalization_kernel.xhtml">arm_compute::NEHOGBlockNormalizationKernel</a>, <a class="el" href="classarm__compute_1_1_n_e_h_o_g_detector_kernel.xhtml">arm_compute::NEHOGDetectorKernel</a>, arm_compute::NEHOGNonMaximaSuppressionKernel / <a class="el" href="classarm__compute_1_1_n_e_h_o_g_descriptor.xhtml">arm_compute::NEHOGDescriptor</a>, <a class="el" href="classarm__compute_1_1_n_e_h_o_g_detector.xhtml">arm_compute::NEHOGDetector</a>, <a class="el" href="classarm__compute_1_1_n_e_h_o_g_gradient.xhtml">arm_compute::NEHOGGradient</a>, <a class="el" href="classarm__compute_1_1_n_e_h_o_g_multi_detection.xhtml">arm_compute::NEHOGMultiDetection</a></li>
543<li><a class="el" href="classarm__compute_1_1_n_e_non_linear_filter_kernel.xhtml">arm_compute::NENonLinearFilterKernel</a> / <a class="el" href="classarm__compute_1_1_n_e_non_linear_filter.xhtml">arm_compute::NENonLinearFilter</a></li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]544</ul>
545</li>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]546<li>Introduced a CLScheduler to manage the default context and command queue used by the runtime library and create synchronisation events.</li>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]547<li>Switched all the kernels / functions to use tensors instead of images.</li>
548<li>Updated documentation to include instructions to build the library from sources.</li>
549</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]550<p>v16.12 Binary preview release</p><ul>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]551<li>Original release</li>
552</ul>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]553<h1><a class="anchor" id="S3_how_to_build"></a>
554How to build the library and the examples</h1>
555<h2><a class="anchor" id="S3_1_build_options"></a>
556Build options</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]557<p>scons 2.3 or above is required to build the library. To see the build options available simply run <code>scons -h</code>: </p><pre class="fragment">debug: Debug (yes|no)
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]558 default: False
559 actual: False
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]560
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]561asserts: Enable asserts (this flag is forced to 1 for debug=1) (yes|no)
562 default: False
563 actual: False
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]564
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]565arch: Target Architecture (armv7a|arm64-v8a|arm64-v8.2-a|x86_32|x86_64)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]566 default: armv7a
567 actual: armv7a
568
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]569os: Target OS (linux|android|bare_metal)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]570 default: linux
571 actual: linux
572
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]573build: Build type (native|cross_compile)
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]574 default: cross_compile
575 actual: cross_compile
576
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]577examples: Build example programs (yes|no)
578 default: True
579 actual: True
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]580
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]581Werror: Enable/disable the -Werror compilation flag (yes|no)
582 default: True
583 actual: True
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]584
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]585opencl: Enable OpenCL support (yes|no)
586 default: True
587 actual: True
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]588
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]589neon: Enable Neon support (yes|no)
590 default: False
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]591 actual: False
592
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]593gles_compute: Enable OpenGL ES Compute Shader support (yes|no)
594 default: False
595 actual: False
596
597embed_kernels: Embed OpenCL kernels and OpenGL ES compute shader in library binary (yes|no)
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]598 default: False
599 actual: False
600
601set_soname: Set the library's soname and shlibversion (requires SCons 2.4 or above) (yes|no)
602 default: False
603 actual: False
604
605openmp: Enable OpenMP backend (yes|no)
606 default: False
607 actual: False
608
609cppthreads: Enable C++11 threads backend (yes|no)
610 default: True
611 actual: True
612
613build_dir: Specify sub-folder for the build ( /path/to/build_dir )
614 default: .
615 actual: .
616
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]617extra_cxx_flags: Extra CXX flags to be appended to the build command
618 default:
619 actual:
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]620
621pmu: Enable PMU counters (yes|no)
622 default: False
623 actual: False
624
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]625mali: Enable Mali hardware counters (yes|no)
626 default: False
627 actual: False
628
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]629validation_tests: Build validation test programs (yes|no)
630 default: False
631 actual: False
632
633benchmark_tests: Build benchmark test programs (yes|no)
634 default: False
635 actual: False
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]636</pre><p><b>debug</b> / <b>asserts:</b> </p><ul>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]637<li>With debug=1 asserts are enabled, and the library is built with symbols and no optimisations enabled.</li>
638<li>With debug=0 and asserts=1: Optimisations are enabled and symbols are removed, however all the asserts are still present (This is about 20% slower than the release build)</li>
639<li>With debug=0 and asserts=0: All optimisations are enable and no validation is performed, if the application misuses the library it is likely to result in a crash. (Only use this mode once you are sure your application is working as expected).</li>
640</ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]641<p><b>arch:</b> The x86_32 and x86_64 targets can only be used with neon=0 and opencl=1.</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]642<p><b>os:</b> Choose the operating system you are targeting: Linux, Android or bare metal. </p><dl class="section note"><dt>Note</dt><dd>bare metal can only be used for NEON (not OpenCL), only static libraries get built and NEON's multi-threading support is disabled.</dd></dl>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]643<p><b>build:</b> you can either build directly on your device (native) or cross compile from your desktop machine (cross-compile). In both cases make sure the compiler is available in your path.</p>
644<dl class="section note"><dt>Note</dt><dd>If you want to natively compile for 32bit on a 64bit ARM device running a 64bit OS then you will have to use cross-compile too.</dd></dl>
645<p><b>Werror:</b> If you are compiling using the same toolchains as the ones used in this guide then there shouldn't be any warning and therefore you should be able to keep Werror=1. If with a different compiler version the library fails to build because of warnings interpreted as errors then, if you are sure the warnings are not important, you might want to try to build with Werror=0 (But please do report the issue either on Github or by an email to <a href="#" onclick="location.href='mai'+'lto:'+'dev'+'el'+'ope'+'r@'+'arm'+'.c'+'om'; return false;">devel<span style="display: none;">.nosp@m.</span>oper<span style="display: none;">.nosp@m.</span>@arm.<span style="display: none;">.nosp@m.</span>com</a> so that the issue can be addressed).</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]646<p><b>opencl</b> / <b>neon</b> / <b>gles_compute:</b> Choose which SIMD technology you want to target. (NEON for ARM Cortex-A CPUs or OpenCL / GLES_COMPUTE for ARM Mali GPUs)</p>
647<p><b>embed_kernels:</b> For OpenCL / GLES_COMPUTE only: set embed_kernels=1 if you want the OpenCL / GLES_COMPUTE kernels to be built in the library's binaries instead of being read from separate ".cl" / ".cs" files. If embed_kernels is set to 0 then the application can set the path to the folder containing the OpenCL / GLES_COMPUTE kernel files by calling CLKernelLibrary::init() / GCKernelLibrary::init(). By default the path is set to "./cl_kernels" / "./cs_shaders".</p>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]648<p><b>set_soname:</b> Do you want to build the versioned version of the library ?</p>
649<p>If enabled the library will contain a SONAME and SHLIBVERSION and some symlinks will automatically be created between the objects. Example: libarm_compute_core.so -&gt; libarm_compute_core.so.1.0.0 libarm_compute_core.so.1 -&gt; libarm_compute_core.so.1.0.0 libarm_compute_core.so.1.0.0</p>
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]650<dl class="section note"><dt>Note</dt><dd>This options is disabled by default as it requires SCons version 2.4 or above.</dd></dl>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]651<p><b>extra_cxx_flags:</b> Custom CXX flags which will be appended to the end of the build command.</p>
652<p><b>build_dir:</b> Build the library in a subfolder of the "build" folder. (Allows to build several configurations in parallel).</p>
653<p><b>examples:</b> Build or not the examples</p>
654<p><b>validation_tests:</b> Enable the build of the validation suite.</p>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]655<p><b>benchmark_tests:</b> Enable the build of the benchmark tests</p>
656<p><b>pmu:</b> Enable the PMU cycle counter to measure execution time in benchmark tests. (Your device needs to support it)</p>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]657<p><b>mali:</b> Enable the collection of Mali hardware counters to measure execution time in benchmark tests. (Your device needs to have a Mali driver that supports it)</p>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]658<p><b>openmp</b> Build in the OpenMP scheduler for NEON.</p>
659<dl class="section note"><dt>Note</dt><dd>Only works when building with g++ not clang++</dd></dl>
660<p><b>cppthreads</b> Build in the C++11 scheduler for NEON.</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]661<dl class="section see"><dt>See also</dt><dd><a class="el" href="classarm__compute_1_1_scheduler.xhtml#aa35fa7aa123444c798c28d4ac8fe7546" title="Sets the user defined scheduler and makes it the active scheduler. ">arm_compute::Scheduler::set</a></dd></dl>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]662<h2><a class="anchor" id="S3_2_linux"></a>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]663Building for Linux</h2>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]664<h3><a class="anchor" id="S3_2_1_library"></a>
665How to build the library ?</h3>
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]666<p>For Linux, the library was successfully built and tested using the following Linaro GCC toolchain:</p>
667<ul>
668<li>gcc-linaro-arm-linux-gnueabihf-4.9-2014.07_linux</li>
669<li>gcc-linaro-4.9-2016.02-x86_64_aarch64-linux-gnu</li>
670<li>gcc-linaro-6.3.1-2017.02-i686_aarch64-linux-gnu</li>
671</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]672<dl class="section note"><dt>Note</dt><dd>If you are building with opencl=1 then scons will expect to find libOpenCL.so either in the current directory or in "build" (See the section below if you need a stub OpenCL library to link against) </dd>
673<dd>
674If you are building with gles_compute=1 then scons will expect to find libEGL.so / libGLESv1_CM.so / libGLESv2.so either in the current directory or in "build" (See the section below if you need a stub OpenCL library to link against)</dd></dl>
675<p>To cross-compile the library in debug mode, with NEON only support, for Linux 32bit: </p><pre class="fragment">scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=linux arch=armv7a
676</pre><p>To cross-compile the library in asserts mode, with OpenCL only support, for Linux 64bit: </p><pre class="fragment">scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=linux arch=arm64-v8a
677</pre><p>To cross-compile the library in asserts mode, with GLES_COMPUTE only support, for Linux 64bit: </p><pre class="fragment">scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=0 gles_compute=1 embed_kernels=1 os=linux arch=arm64-v8a
678</pre><p>You can also compile the library natively on an ARM device by using <b>build=native</b>: </p><pre class="fragment">scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=arm64-v8a build=native
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]679scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=native
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]680</pre><dl class="section note"><dt>Note</dt><dd>g++ for ARM is mono-arch, therefore if you want to compile for Linux 32bit on a Linux 64bit platform you will have to use a cross compiler.</dd></dl>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]681<p>For example on a 64bit Debian based system you would have to install <b>g++-arm-linux-gnueabihf</b> </p><pre class="fragment">apt-get install g++-arm-linux-gnueabihf
682</pre><p>Then run </p><pre class="fragment">scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=cross_compile
683</pre><p>or simply remove the build parameter as build=cross_compile is the default value: </p><pre class="fragment">scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a
Anthony Barbiera4376382017-04-12 15:12:46 +0100[diff] [blame]684</pre><dl class="section attention"><dt>Attention</dt><dd>To cross compile with opencl=1 you need to make sure to have a version of libOpenCL matching your target architecture.</dd></dl>
685<h3><a class="anchor" id="S3_2_2_examples"></a>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]686How to manually build the examples ?</h3>
687<p>The examples get automatically built by scons as part of the build process of the library described above. This section just describes how you can build and link your own application against our library.</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]688<dl class="section note"><dt>Note</dt><dd>The following command lines assume the <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> and libOpenCL binaries are present in the current directory or in the system library path. If this is not the case you can specify the location of the pre-built library with the compiler option -L. When building the OpenCL example the commands below assume that the CL headers are located in the include folder where the command is executed.</dd></dl>
689<p>To cross compile a NEON example for Linux 32bit: </p><pre class="fragment">arm-linux-gnueabihf-g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute -larm_compute_core -o neon_convolution
690</pre><p>To cross compile a NEON example for Linux 64bit: </p><pre class="fragment">aarch64-linux-gnu-g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute -larm_compute_core -o neon_convolution
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]691</pre><p>(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]692<p>To cross compile an OpenCL example for Linux 32bit: </p><pre class="fragment">arm-linux-gnueabihf-g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute -larm_compute_core -lOpenCL -o cl_convolution -DARM_COMPUTE_CL
693</pre><p>To cross compile an OpenCL example for Linux 64bit: </p><pre class="fragment">aarch64-linux-gnu-g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute -larm_compute_core -lOpenCL -o cl_convolution -DARM_COMPUTE_CL
694</pre><p>To cross compile a GLES example for Linux 32bit: </p><pre class="fragment">arm-linux-gnueabihf-g++ examples/gc_absdiff.cpp utils/Utils.cpp -I. -Iinclude/ -L. -larm_compute -larm_compute_core -std=c++11 -mfpu=neon -DARM_COMPUTE_GC -Iinclude/linux/ -o gc_absdiff
695</pre><p>To cross compile a GLES example for Linux 64bit: </p><pre class="fragment">aarch64-linux-gnu-g++ examples/gc_absdiff.cpp utils/Utils.cpp -I. -Iinclude/ -L. -larm_compute -larm_compute_core -std=c++11 -DARM_COMPUTE_GC -Iinclude/linux/ -o gc_absdiff
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]696</pre><p>(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]697<p>To cross compile the examples with the Graph API, such as <a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a>, you need to link the examples against arm_compute_graph.so too.</p>
698<dl class="section note"><dt>Note</dt><dd>The compute library must currently be built with both neon and opencl enabled - neon=1 and opencl=1</dd></dl>
699<p>i.e. to cross compile the "graph_lenet" example for Linux 32bit: </p><pre class="fragment">arm-linux-gnueabihf-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -Wl,--allow-shlib-undefined -o graph_lenet
700</pre><p>i.e. to cross compile the "graph_lenet" example for Linux 64bit: </p><pre class="fragment">aarch64-linux-gnu-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute_graph -larm_compute -larm_compute_core -Wl,--allow-shlib-undefined -o graph_lenet
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]701</pre><p>(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]702<dl class="section note"><dt>Note</dt><dd>If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a>, arm_compute_core</dd></dl>
703<p>To compile natively (i.e directly on an ARM device) for NEON for Linux 32bit: </p><pre class="fragment">g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -larm_compute -larm_compute_core -o neon_convolution
704</pre><p>To compile natively (i.e directly on an ARM device) for NEON for Linux 64bit: </p><pre class="fragment">g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute -larm_compute_core -o neon_convolution
Anthony Barbier46d59272017-05-04 09:15:15 +0100[diff] [blame]705</pre><p>(notice the only difference with the 32 bit command is that we don't need the -mfpu option)</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]706<p>To compile natively (i.e directly on an ARM device) for OpenCL for Linux 32bit or Linux 64bit: </p><pre class="fragment">g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute -larm_compute_core -lOpenCL -o cl_convolution -DARM_COMPUTE_CL
707</pre><p>To compile natively (i.e directly on an ARM device) for GLES for Linux 32bit or Linux 64bit: </p><pre class="fragment">g++ examples/gc_absdiff.cpp utils/Utils.cpp -I. -Iinclude/ -L. -larm_compute -larm_compute_core -std=c++11 -DARM_COMPUTE_GC -Iinclude/linux/ -o gc_absdiff
708</pre><p>To compile natively the examples with the Graph API, such as <a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a>, you need to link the examples against arm_compute_graph.so too. </p><dl class="section note"><dt>Note</dt><dd>The compute library must currently be built with both neon and opencl enabled - neon=1 and opencl=1</dd></dl>
709<p>i.e. to natively compile the "graph_lenet" example for Linux 32bit: </p><pre class="fragment">g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute_graph -larm_compute -larm_compute_core -Wl,--allow-shlib-undefined -o graph_lenet
710</pre><p>i.e. to natively compile the "graph_lenet" example for Linux 64bit: </p><pre class="fragment">g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 L. -larm_compute_graph -larm_compute -larm_compute_core -Wl,--allow-shlib-undefined -o graph_lenet
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]711</pre><p>(notice the only difference with the 32 bit command is that we don't need the -mfpu option)</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]712<dl class="section note"><dt>Note</dt><dd>If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a>, arm_compute_core</dd>
713<dd>
714These two commands assume libarm_compute.so is available in your library path, if not add the path to it using -L</dd></dl>
715<p>To run the built executable simply run: </p><pre class="fragment">LD_LIBRARY_PATH=build ./neon_convolution
716</pre><p>or </p><pre class="fragment">LD_LIBRARY_PATH=build ./cl_convolution
717</pre><h2><a class="anchor" id="S3_3_android"></a>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]718Building for Android</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]719<p>For Android, the library was successfully built and tested using Google's standalone toolchains:</p><ul>
720<li>NDK r14 arm-linux-androideabi-4.9 for armv7a (clang++)</li>
721<li>NDK r14 aarch64-linux-android-4.9 for arm64-v8a (clang++)</li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]722</ul>
723<p>Here is a guide to <a href="https://developer.android.com/ndk/guides/standalone_toolchain.html">create your Android standalone toolchains from the NDK</a></p>
724<ul>
Anthony Barbierdbdab852017-06-23 15:42:00 +0100[diff] [blame]725<li>Download the NDK r14 from here: <a href="https://developer.android.com/ndk/downloads/index.html">https://developer.android.com/ndk/downloads/index.html</a></li>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]726<li>Make sure you have Python 2 installed on your machine.</li>
727<li>Generate the 32 and/or 64 toolchains by running the following commands:</li>
728</ul>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]729<pre class="fragment">$NDK/build/tools/make_standalone_toolchain.py --arch arm64 --install-dir $MY_TOOLCHAINS/aarch64-linux-android-4.9 --stl gnustl --api 21
730$NDK/build/tools/make_standalone_toolchain.py --arch arm --install-dir $MY_TOOLCHAINS/arm-linux-androideabi-4.9 --stl gnustl --api 21
731</pre><dl class="section attention"><dt>Attention</dt><dd>Due to some NDK issues make sure you use clang++ &amp; gnustl</dd></dl>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]732<dl class="section note"><dt>Note</dt><dd>Make sure to add the toolchains to your PATH: export PATH=$PATH:$MY_TOOLCHAINS/aarch64-linux-android-4.9/bin:$MY_TOOLCHAINS/arm-linux-androideabi-4.9/bin</dd></dl>
733<h3><a class="anchor" id="S3_3_1_library"></a>
734How to build the library ?</h3>
735<dl class="section note"><dt>Note</dt><dd>If you are building with opencl=1 then scons will expect to find libOpenCL.so either in the current directory or in "build" (See the section below if you need a stub OpenCL library to link against)</dd></dl>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]736<p>To cross-compile the library in debug mode, with NEON only support, for Android 32bit: </p><pre class="fragment">CXX=clang++ CC=clang scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=android arch=armv7a
737</pre><p>To cross-compile the library in asserts mode, with OpenCL only support, for Android 64bit: </p><pre class="fragment">CXX=clang++ CC=clang scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=android arch=arm64-v8a
738</pre><p>To cross-compile the library in asserts mode, with GLES_COMPUTE only support, for Android 64bit: </p><pre class="fragment">CXX=clang++ CC=clang scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=0 gles_compute=1 embed_kernels=1 os=android arch=arm64-v8a
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]739</pre><h3><a class="anchor" id="S3_3_2_examples"></a>
740How to manually build the examples ?</h3>
741<p>The examples get automatically built by scons as part of the build process of the library described above. This section just describes how you can build and link your own application against our library.</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]742<dl class="section note"><dt>Note</dt><dd>The following command lines assume the <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> and libOpenCL binaries are present in the current directory or in the system library path. If this is not the case you can specify the location of the pre-built library with the compiler option -L. When building the OpenCL example the commands below assume that the CL headers are located in the include folder where the command is executed.</dd></dl>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]743<p>Once you've got your Android standalone toolchain built and added to your path you can do the following:</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]744<p>To cross compile a NEON example: </p><pre class="fragment">#32 bit:
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]745arm-linux-androideabi-clang++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o neon_convolution_arm -static-libstdc++ -pie
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]746#64 bit:
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]747aarch64-linux-android-clang++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o neon_convolution_aarch64 -static-libstdc++ -pie
748</pre><p>To cross compile an OpenCL example: </p><pre class="fragment">#32 bit:
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]749arm-linux-androideabi-clang++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o cl_convolution_arm -static-libstdc++ -pie -lOpenCL -DARM_COMPUTE_CL
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]750#64 bit:
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]751aarch64-linux-android-clang++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o cl_convolution_aarch64 -static-libstdc++ -pie -lOpenCL -DARM_COMPUTE_CL
752</pre><p>To cross compile a GLES example: #32 bit: arm-linux-androideabi-clang++ <a class="el" href="gc__absdiff_8cpp.xhtml">examples/gc_absdiff.cpp</a> <a class="el" href="utils_2_utils_8cpp.xhtml">utils/Utils.cpp</a> -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o gc_absdiff_arm -static-libstdc++ -pie -DARM_COMPUTE_GC #64 bit: aarch64-linux-android-clang++ <a class="el" href="gc__absdiff_8cpp.xhtml">examples/gc_absdiff.cpp</a> <a class="el" href="utils_2_utils_8cpp.xhtml">utils/Utils.cpp</a> -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o gc_absdiff_aarch64 -static-libstdc++ -pie -DARM_COMPUTE_GC</p>
753<p>To cross compile the examples with the Graph API, such as <a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a>, you need to link the library arm_compute_graph also. (notice the compute library has to be built with both neon and opencl enabled - neon=1 and opencl=1) </p><pre class="fragment">#32 bit:
754arm-linux-androideabi-clang++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -Wl,--whole-archive -larm_compute_graph-static -Wl,--no-whole-archive -larm_compute-static -larm_compute_core-static -L. -o graph_lenet_arm -static-libstdc++ -pie -lOpenCL -DARM_COMPUTE_CL
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]755#64 bit:
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]756aarch64-linux-android-clang++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp -I. -Iinclude -std=c++11 -Wl,--whole-archive -larm_compute_graph-static -Wl,--no-whole-archive -larm_compute-static -larm_compute_core-static -L. -o graph_lenet_aarch64 -static-libstdc++ -pie -lOpenCL -DARM_COMPUTE_CL
757</pre><dl class="section note"><dt>Note</dt><dd>Due to some issues in older versions of the Mali OpenCL DDK (&lt;= r13p0), we recommend to link <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> statically on Android. </dd>
758<dd>
759When linked statically the arm_compute_graph library currently needs the &ndash;whole-archive linker flag in order to work properly</dd></dl>
760<p>Then you need to do is upload the executable and the shared library to the device using ADB: </p><pre class="fragment">adb push neon_convolution_arm /data/local/tmp/
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]761adb push cl_convolution_arm /data/local/tmp/
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]762adb push gc_absdiff_arm /data/local/tmp/
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]763adb shell chmod 777 -R /data/local/tmp/
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]764</pre><p>And finally to run the example: </p><pre class="fragment">adb shell /data/local/tmp/neon_convolution_arm
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]765adb shell /data/local/tmp/cl_convolution_arm
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]766adb shell /data/local/tmp/gc_absdiff_arm
767</pre><p>For 64bit: </p><pre class="fragment">adb push neon_convolution_aarch64 /data/local/tmp/
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]768adb push cl_convolution_aarch64 /data/local/tmp/
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]769adb push gc_absdiff_aarch64 /data/local/tmp/
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]770adb shell chmod 777 -R /data/local/tmp/
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]771</pre><p>And finally to run the example: </p><pre class="fragment">adb shell /data/local/tmp/neon_convolution_aarch64
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]772adb shell /data/local/tmp/cl_convolution_aarch64
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]773adb shell /data/local/tmp/gc_absdiff_aarch64
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]774</pre><h2><a class="anchor" id="S3_4_bare_metal"></a>
775Building for bare metal</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]776<p>For bare metal, the library was successfully built using linaros's latest (gcc-linaro-6.3.1-2017.05) bare metal toolchains:</p><ul>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]777<li>arm-eabi for armv7a</li>
778<li>aarch64-elf for arm64-v8a</li>
779</ul>
780<p>Download linaro for <a href="https://releases.linaro.org/components/toolchain/binaries/6.3-2017.05/arm-eabi/">armv7a</a> and <a href="https://releases.linaro.org/components/toolchain/binaries/6.3-2017.05/aarch64-elf/">arm64-v8a</a>.</p>
781<dl class="section note"><dt>Note</dt><dd>Make sure to add the toolchains to your PATH: export PATH=$PATH:$MY_TOOLCHAINS/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-elf/bin:$MY_TOOLCHAINS/gcc-linaro-6.3.1-2017.05-x86_64_arm-eabi/bin</dd></dl>
782<h3><a class="anchor" id="S3_4_1_library"></a>
783How to build the library ?</h3>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]784<p>To cross-compile the library with NEON support for baremetal arm64-v8a: </p><pre class="fragment">scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=bare_metal arch=arm64-v8a build=cross_compile cppthreads=0 openmp=0 standalone=1
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]785</pre><h3><a class="anchor" id="S3_4_2_examples"></a>
786How to manually build the examples ?</h3>
787<p>Examples are disabled when building for bare metal. If you want to build the examples you need to provide a custom bootcode depending on the target architecture and link against the compute library. More information about bare metal bootcode can be found <a href="http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.dai0527a/index.html">here</a>.</p>
788<h2><a class="anchor" id="S3_5_windows_host"></a>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]789Building on a Windows host system</h2>
790<p>Using <code>scons</code> directly from the Windows command line is known to cause problems. The reason seems to be that if <code>scons</code> is setup for cross-compilation it gets confused about Windows style paths (using backslashes). Thus it is recommended to follow one of the options outlined below.</p>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]791<h3><a class="anchor" id="S3_5_1_ubuntu_on_windows"></a>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]792Bash on Ubuntu on Windows</h3>
793<p>The best and easiest option is to use <a href="https://msdn.microsoft.com/en-gb/commandline/wsl/about">Ubuntu on Windows</a>. This feature is still marked as <em>beta</em> and thus might not be available. However, if it is building the library is as simple as opening a <em>Bash on Ubuntu on Windows</em> shell and following the general guidelines given above.</p>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]794<h3><a class="anchor" id="S3_5_2_cygwin"></a>
Kaizen8938bd32017-09-28 14:38:23 +0100[diff] [blame]795Cygwin</h3>
796<p>If the Windows subsystem for Linux is not available <a href="https://www.cygwin.com/">Cygwin</a> can be used to install and run <code>scons</code>. In addition to the default packages installed by Cygwin <code>scons</code> has to be selected in the installer. (<code>git</code> might also be useful but is not strictly required if you already have got the source code of the library.) Linaro provides pre-built versions of <a href="http://releases.linaro.org/components/toolchain/binaries/">GCC cross-compilers</a> that can be used from the Cygwin terminal. When building for Android the compiler is included in the Android standalone toolchain. After everything has been set up in the Cygwin terminal the general guide on building the library can be followed.</p>
Kaizenbf8b01d2017-10-12 14:26:51 +0100[diff] [blame]797<h2><a class="anchor" id="S3_6_cl_stub_library"></a>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]798The OpenCL stub library</h2>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]799<p>In the opencl-1.2-stubs folder you will find the sources to build a stub OpenCL library which then can be used to link your application or <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> against.</p>
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]800<p>If you preferred you could retrieve the OpenCL library from your device and link against this one but often this library will have dependencies on a range of system libraries forcing you to link your application against those too even though it is not using them.</p>
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]801<dl class="section warning"><dt>Warning</dt><dd>This OpenCL library provided is a stub and <em>not</em> a real implementation. You can use it to resolve OpenCL's symbols in <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> while building the example but you must make sure the real libOpenCL.so is in your PATH when running the example or it will not work.</dd></dl>
802<p>To cross-compile the stub OpenCL library simply run: </p><pre class="fragment">&lt;target-prefix&gt;-gcc -o libOpenCL.so -Iinclude opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
803</pre><p>For example: </p><pre class="fragment">#Linux 32bit
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]804arm-linux-gnueabihf-gcc -o libOpenCL.so -Iinclude opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
805#Linux 64bit
806aarch64-linux-gnu-gcc -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC
807#Android 32bit
808arm-linux-androideabi-clang -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
809#Android 64bit
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000[diff] [blame]810aarch64-linux-android-clang -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
811</pre><h2><a class="anchor" id="S3_7_gles_stub_library"></a>
812The Linux OpenGLES and EGL stub libraries</h2>
813<p>In the opengles-3.1-stubs folder you will find the sources to build stub EGL and OpenGLES libraries which then can be used to link your Linux application of <a class="el" href="namespacearm__compute.xhtml" title="This file contains all available output stages for GEMMLowp on OpenCL. ">arm_compute</a> against.</p>
814<dl class="section note"><dt>Note</dt><dd>The stub libraries are only needed on Linux. For Android, the NDK toolchains already provide the meta-EGL and meta-GLES libraries.</dd></dl>
815<p>To cross-compile the stub OpenGLES and EGL libraries simply run: </p><pre class="fragment">&lt;target-prefix&gt;-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
816&lt;target-prefix&gt;-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared
817
818#Linux 32bit
819arm-linux-gnueabihf-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
820arm-linux-gnueabihf-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared
821
822#Linux 64bit
823aarch64-linux-gnu-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
824aarch64-linux-gnu-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared</pre> </div></div><!-- contents -->
Anthony Barbier871448e2017-03-24 14:54:29 +0000[diff] [blame]825</div><!-- doc-content -->
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