aboutsummaryrefslogtreecommitdiff
path: root/docs/00_introduction.dox
blob: 1d309cb80fd46d15b9a241f2caf55c378462aa5a (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
namespace arm_compute
{
/** @mainpage Introduction

@tableofcontents

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:
 - OS: Linux, Android or bare metal.
 - Architecture: armv7a (32bit) or arm64-v8a (64bit)
 - Technology: NEON / OpenCL / GLES_COMPUTE / NEON and OpenCL and GLES_COMPUTE
 - 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.

@section S0_1_contact Contact / Support

Please email developer@arm.com

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:

    $ strings android-armv7a-cl-asserts/libarm_compute.so | grep arm_compute_version
    arm_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

@section S0_2_prebuilt_binaries Pre-built binaries

For each release we provide some pre-built binaries of the library [here](https://github.com/ARM-software/ComputeLibrary/releases)

These binaries have been built using the following toolchains:
            - Linux armv7a: gcc-linaro-arm-linux-gnueabihf-4.9-2014.07_linux
            - Linux arm64-v8a: gcc-linaro-4.9-2016.02-x86_64_aarch64-linux-gnu
            - Android armv7a: clang++ / gnustl NDK r16b
            - Android am64-v8a: clang++ / gnustl NDK r16b

@warning Make sure to use a compatible toolchain to build your application or you will get some std::bad_alloc errors at runtime.

@section S1_file_organisation File organisation

This archive contains:
 - The arm_compute header and source files
 - The latest Khronos OpenCL 1.2 C headers from the <a href="https://www.khronos.org/registry/cl/">Khronos OpenCL registry</a>
 - 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)
 - The latest Khronos OpenGL ES 3.1 C headers from the <a href="https://www.khronos.org/registry/gles/">Khronos OpenGL ES registry</a>
 - The latest Khronos EGL 1.5 C headers from the <a href="https://www.khronos.org/registry/gles/">Khronos EGL registry</a>
 - The sources for a stub version of libOpenCL.so, libGLESv1_CM.so, libGLESv2.so and libEGL.so to help you build your application.
 - An examples folder containing a few examples to compile and link against the library.
 - A @ref utils folder containing headers with some boiler plate code used by the examples.
 - This documentation.

You should have the following file organisation:

	.
	├── arm_compute --> All the arm_compute headers
	│   ├── core
	│   │   ├── CL
	│   │   │   ├── CLKernelLibrary.h --> Manages all the OpenCL kernels compilation and caching, provides accessors for the OpenCL Context.
	│   │   │   ├── CLKernels.h --> Includes all the OpenCL kernels at once
	│   │   │   ├── CL specialisation of all the generic objects interfaces (ICLTensor, ICLImage, etc.)
	│   │   │   ├── kernels --> Folder containing all the OpenCL kernels
	│   │   │   │   └── CL*Kernel.h
	│   │   │   └── OpenCL.h --> Wrapper to configure the Khronos OpenCL C++ header
	│   │   ├── CPP
	│   │   │   ├── CPPKernels.h --> Includes all the CPP kernels at once
	│   │   │   └── kernels --> Folder containing all the CPP kernels
	│   │   │       └── CPP*Kernel.h
	│   │   ├── GLES_COMPUTE
	│   │   │   ├── GCKernelLibrary.h --> Manages all the GLES kernels compilation and caching, provides accessors for the GLES Context.
	│   │   │   ├── GCKernels.h --> Includes all the GLES kernels at once
	│   │   │   ├── GLES specialisation of all the generic objects interfaces (IGCTensor, IGCImage, etc.)
	│   │   │   ├── kernels --> Folder containing all the GLES kernels
	│   │   │   │   └── GC*Kernel.h
	│   │   │   └── OpenGLES.h --> Wrapper to configure the Khronos EGL and OpenGL ES C header
	│   │   ├── NEON
	│   │   │   ├── kernels --> Folder containing all the NEON kernels
	│   │   │   │   ├── arm64 --> Folder containing the interfaces for the assembly arm64 NEON kernels
	│   │   │   │   ├── arm32 --> Folder containing the interfaces for the assembly arm32 NEON kernels
	│   │   │   │   ├── assembly --> Folder containing the NEON assembly routines.
	│   │   │   │   └── NE*Kernel.h
	│   │   │   └── NEKernels.h --> Includes all the NEON kernels at once
	│   │   ├── All common basic types (Types.h, Window, Coordinates, Iterator, etc.)
	│   │   ├── All generic objects interfaces (ITensor, IImage, etc.)
	│   │   └── Objects metadata classes (ImageInfo, TensorInfo, MultiImageInfo)
	│   ├── graph
	│   │   ├── CL --> OpenCL specific operations
	│   │   │   └── CLMap.h / CLUnmap.h
	│   │   ├── nodes
	│   │   │   └── The various nodes supported by the graph API
	│   │   ├── Nodes.h --> Includes all the Graph nodes at once.
	│   │   └── Graph objects ( INode, ITensorAccessor, Graph, etc.)
	│   └── runtime
	│       ├── CL
	│       │   ├── CL objects & allocators (CLArray, CLImage, CLTensor, etc.)
	│       │   ├── functions --> Folder containing all the OpenCL functions
	│       │   │   └── CL*.h
	│       │   ├── CLScheduler.h --> Interface to enqueue OpenCL kernels and get/set the OpenCL CommandQueue and ICLTuner.
	│       │   └── CLFunctions.h --> Includes all the OpenCL functions at once
	│       ├── CPP
	│       │   ├── CPPKernels.h --> Includes all the CPP functions at once.
	│       │   └── CPPScheduler.h --> Basic pool of threads to execute CPP/NEON code on several cores in parallel
	│       ├── GLES_COMPUTE
	│       │   ├── GLES objects & allocators (GCArray, GCImage, GCTensor, etc.)
	│       │   ├── functions --> Folder containing all the GLES functions
	│       │   │   └── GC*.h
	│       │   ├── GCScheduler.h --> Interface to enqueue GLES kernels and get/set the GLES CommandQueue.
	│       │   └── GCFunctions.h --> Includes all the GLES functions at once
	│       ├── NEON
	│       │   ├── functions --> Folder containing all the NEON functions
	│       │   │   └── NE*.h
	│       │   └── NEFunctions.h --> Includes all the NEON functions at once
	│       ├── OMP
	│       │   └── OMPScheduler.h --> OpenMP scheduler (Alternative to the CPPScheduler)
	│       ├── Memory manager files (LifetimeManager, PoolManager, etc.)
	│       └── Basic implementations of the generic object interfaces (Array, Image, Tensor, etc.)
	├── data -> Contains test images and reference data dumps used by validation tests
	├── docs -> Contains Doxyfile and Doxygen sources used to generate the HTML pages in the documentation folder.
	├── documentation
	│   ├── index.xhtml
	│   └── ...
	├── documentation.xhtml -> documentation/index.xhtml
	├── examples
	│   ├── cl_*.cpp --> OpenCL examples
	│   ├── gc_*.cpp --> GLES compute shaders examples
	│   ├── graph_*.cpp --> Graph examples
	│   ├── neoncl_*.cpp --> NEON / OpenCL interoperability examples
	│   └── neon_*.cpp --> NEON examples
	├── include
	│   ├── CL
	│   │   └── Khronos OpenCL C headers and C++ wrapper
	│   ├── half --> FP16 library available from http://half.sourceforge.net
	│   ├── libnpy --> Library to load / write npy buffers, available from https://github.com/llohse/libnpy
	│   └── linux --> Headers only needed for Linux builds
	│       └── Khronos EGL and OpenGLES headers
	├── opencl-1.2-stubs
	│   └── opencl_stubs.c --> OpenCL stubs implementation
	├── opengles-3.1-stubs
	│   ├── EGL.c --> EGL stubs implementation
	│   └── GLESv2.c --> GLESv2 stubs implementation
	├── scripts
	│   ├── caffe_data_extractor.py --> Basic script to export weights from Caffe to npy files
	│   └── tensorflow_data_extractor.py --> Basic script to export weights from Tensor Flow to npy files
	├── src
	│   ├── core
	│   │   └── ... (Same structure as headers)
	│   │       ├── CL
	│   │       │   └── cl_kernels --> All the OpenCL kernels
	│   │       └── GLES_COMPUTE
	│   │           └── cs_shaders --> All the OpenGL ES Compute Shaders
	│   ├── graph
	│   │   └── ... (Same structure as headers)
	│   └── runtime
	│       └── ... (Same structure as headers)
	├── support
	│   └── Various headers to work around toolchains / platform issues.
	├── tests
	│   ├── All test related files shared between validation and benchmark
	│   ├── CL --> OpenCL accessors
	│   ├── GLES_COMPUTE --> GLES accessors
	│   ├── NEON --> NEON accessors
	│   ├── benchmark --> Sources for benchmarking
	│   │   ├── Benchmark specific files
	│   │   ├── CL --> OpenCL benchmarking tests
	│   │   ├── GLES_COMPUTE --> GLES benchmarking tests
	│   │   ├── fixtures
	│   │   │   └── Fixtures to initialise and run the runtime Functions.
	│   │   └── NEON --> NEON benchmarking tests
	│   ├── datasets
	│   │   └── Datasets for all the validation / benchmark tests, layer configurations for various networks, etc.
	│   ├── framework
	│   │   └── Boiler plate code for both validation and benchmark test suites (Command line parsers, instruments, output loggers, etc.)
	│   ├── networks
	│   │   └── Examples of how to instantiate networks.
	│   ├── validation --> Sources for validation
	│   │   ├── Validation specific files
	│   │   ├── CL --> OpenCL validation tests
	│   │   ├── GLES_COMPUTE --> GLES validation tests
	│   │   ├── CPP --> C++ reference implementations
	│   │   ├── fixtures
	│   │   │   └── Fixtures to initialise and run the runtime Functions.
	│   │   └── NEON --> NEON validation tests
	│   └── dataset --> Datasets defining common sets of input parameters
	└── utils --> Boiler plate code used by examples
	    └── Various utilities to print types, load / store assets, etc.

@section S2_versions_changelog Release versions and changelog

@subsection S2_1_versions Release versions

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:

	v17.03 (First release of March 2017)
	v17.03.1 (Second release of March 2017)
	v17.04 (First release of April 2017)

@note We're aiming at releasing one major public release with new features per quarter. All releases in between will only contain bug fixes.

@subsection S2_2_changelog Changelog

v18.05 Public maintenance release
 - Major redesign in the interface for the neon kernels implemented in assembly.
 - Removed arm_compute::NEGEMMLowpAArch64A53Kernel / arm_compute::NEGEMMLowpAArch64Kernel / arm_compute::NEGEMMLowpAArch64V8P4Kernel / arm_compute::NEGEMMInterleavedBlockedKernel / arm_compute::NEGEMMLowpAssemblyMatrixMultiplyCore / arm_compute::NEHGEMMAArch64FP16Kernel
 - Added NEGEMMAssemblyWrapper and AssemblyKernelGlue which are used to execute assembly kernels in neon functions.
 - Minor changes to the CPUInfo type to make it compatible with the new assembly gemm interface.

v18.03 Public maintenance release
 - Various bug fixes.
 - Fixed bug in @ref NEActivationLayer
 - Fix in @ref CLTuner when using batches.
 - Updated recommended NDK version to r16b (And fixed warnings).
 - Fixed bug in validation code.
 - Added Inception v4 graph example.
 - Renamed NEWinogradLayer.cpp to @ref NEWinogradConvolutionLayer

v18.02 Public major release
 - Various NEON / OpenCL / GLES optimisations.
 - Various bug fixes.
 - Changed default number of threads on big LITTLE systems.
 - Refactored examples and added:
    - graph_mobilenet_qassym8
    - graph_resnet
    - graph_squeezenet_v1_1
 - Renamed @ref CLConvolutionLayer into @ref CLGEMMConvolutionLayer and created a new @ref CLConvolutionLayer to select the fastest convolution method.
 - Renamed @ref NEConvolutionLayer into @ref NEGEMMConvolutionLayer and created a new @ref NEConvolutionLayer to select the fastest convolution method.
 - Added in place support to:
    - @ref CLActivationLayer
    - @ref CLBatchNormalizationLayer
 - Added QASYMM8 support to:
    - @ref CLActivationLayer
    - @ref CLDepthwiseConvolutionLayer
    - @ref NEDepthwiseConvolutionLayer
    - @ref NESoftmaxLayer
 - Added FP16 support to:
    - @ref CLDepthwiseConvolutionLayer3x3
    - @ref CLDepthwiseConvolutionLayer
 - Added broadcasting support to @ref NEArithmeticAddition / @ref CLArithmeticAddition / @ref CLPixelWiseMultiplication
 - Added fused batched normalization and activation to @ref CLBatchNormalizationLayer and @ref NEBatchNormalizationLayer
 - Added support for non-square pooling to @ref NEPoolingLayer and @ref CLPoolingLayer
 - New OpenCL kernels / functions:
    - @ref CLDirectConvolutionLayerOutputStageKernel
 - New NEON kernels / functions
    - Added name() method to all kernels.
    - Added support for Winograd 5x5.
    - @ref NEPermuteKernel / @ref NEPermute
    - @ref NEWinogradLayerTransformInputKernel / NEWinogradLayer
    - @ref NEWinogradLayerTransformOutputKernel / NEWinogradLayer
    - @ref NEWinogradLayerTransformWeightsKernel / NEWinogradLayer
    - Renamed NEWinogradLayerKernel into @ref NEWinogradLayerBatchedGEMMKernel
 - New GLES kernels / functions:
    - @ref GCTensorShiftKernel / @ref GCTensorShift

v18.01 Public maintenance release
 - Various bug fixes
 - Added some of the missing validate() methods
 - Added @ref CLDeconvolutionLayerUpsampleKernel / @ref CLDeconvolutionLayer @ref CLDeconvolutionLayerUpsample
 - Added @ref CLPermuteKernel / @ref CLPermute
 - Added method to clean the programs cache in the CL Kernel library.
 - Added @ref GCArithmeticAdditionKernel / @ref GCArithmeticAddition
 - Added @ref GCDepthwiseConvolutionLayer3x3Kernel / @ref GCDepthwiseConvolutionLayer3x3
 - Added @ref GCNormalizePlanarYUVLayerKernel / @ref GCNormalizePlanarYUVLayer
 - Added @ref GCScaleKernel / @ref GCScale
 - Added @ref GCWeightsReshapeKernel / @ref GCConvolutionLayer
 - Added FP16 support to the following GLES compute kernels:
    - @ref GCCol2ImKernel
    - @ref GCGEMMInterleave4x4Kernel
    - @ref GCGEMMTranspose1xWKernel
    - @ref GCIm2ColKernel
 - Refactored NEON Winograd (NEWinogradLayerKernel)
 - Added @ref NEDirectConvolutionLayerOutputStageKernel
 - Added QASYMM8 support to the following NEON kernels:
    - @ref NEDepthwiseConvolutionLayer3x3Kernel
    - @ref NEFillBorderKernel
    - @ref NEPoolingLayerKernel
 - Added new examples:
    - graph_cl_mobilenet_qasymm8.cpp
    - graph_inception_v3.cpp
    - gc_dc.cpp
 - More tests added to both validation and benchmarking suites.

v17.12 Public major release
 - Most machine learning functions on OpenCL support the new data type QASYMM8
 - Introduced logging interface
 - Introduced opencl timer
 - Reworked GEMMLowp interface
 - Added new NEON assembly kernels for GEMMLowp, SGEMM and HGEMM
 - Added validation method for most Machine Learning kernels / functions
 - Added new graph examples such as googlenet, mobilenet, squeezenet, vgg16 and vgg19
 - Added sgemm example for OpenCL
 - Added absolute difference example for GLES compute
 - Added new tests and benchmarks in validation and benchmark frameworks
 - Added new kernels / functions for GLES compute

 - New OpenGL ES kernels / functions
    - @ref GCAbsoluteDifferenceKernel / @ref GCAbsoluteDifference
    - @ref GCActivationLayerKernel / @ref GCActivationLayer
    - @ref GCBatchNormalizationLayerKernel / @ref GCBatchNormalizationLayer
    - @ref GCCol2ImKernel
    - @ref GCDepthConcatenateLayerKernel / @ref GCDepthConcatenateLayer
    - @ref GCDirectConvolutionLayerKernel / @ref GCDirectConvolutionLayer
    - @ref GCDropoutLayerKernel / @ref GCDropoutLayer
    - @ref GCFillBorderKernel / @ref GCFillBorder
    - @ref GCGEMMInterleave4x4Kernel / @ref GCGEMMInterleave4x4
    - @ref GCGEMMMatrixAccumulateBiasesKernel / @ref GCGEMMMatrixAdditionKernel / @ref GCGEMMMatrixMultiplyKernel / @ref GCGEMM
    - @ref GCGEMMTranspose1xWKernel / @ref GCGEMMTranspose1xW
    - @ref GCIm2ColKernel
    - @ref GCNormalizationLayerKernel / @ref GCNormalizationLayer
    - @ref GCPixelWiseMultiplicationKernel / @ref GCPixelWiseMultiplication
    - @ref GCPoolingLayerKernel / @ref GCPoolingLayer
    - @ref GCLogits1DMaxKernel / @ref GCLogits1DShiftExpSumKernel / @ref GCLogits1DNormKernel / @ref GCSoftmaxLayer
    - @ref GCTransposeKernel / @ref GCTranspose

 - New NEON kernels / functions
    - arm_compute::NEGEMMLowpAArch64A53Kernel / arm_compute::NEGEMMLowpAArch64Kernel / arm_compute::NEGEMMLowpAArch64V8P4Kernel / arm_compute::NEGEMMInterleavedBlockedKernel / arm_compute::NEGEMMLowpAssemblyMatrixMultiplyCore
    - arm_compute::NEHGEMMAArch64FP16Kernel
    - @ref NEDepthwiseConvolutionLayer3x3Kernel / @ref NEDepthwiseIm2ColKernel / @ref NEGEMMMatrixVectorMultiplyKernel / @ref NEDepthwiseVectorToTensorKernel / @ref NEDepthwiseConvolutionLayer
    - @ref NEGEMMLowpOffsetContributionKernel / @ref NEGEMMLowpMatrixAReductionKernel / @ref NEGEMMLowpMatrixBReductionKernel / @ref NEGEMMLowpMatrixMultiplyCore
    - @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
    - @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale
    - NEWinogradLayer / NEWinogradLayerKernel

 - New OpenCL kernels / functions
    - @ref CLGEMMLowpOffsetContributionKernel / @ref CLGEMMLowpMatrixAReductionKernel / @ref CLGEMMLowpMatrixBReductionKernel / @ref CLGEMMLowpMatrixMultiplyCore
    - @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel / @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
    - @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel / @ref CLGEMMLowpQuantizeDownInt32ToUint8Scale

 - New graph nodes for NEON and OpenCL
    - graph::BranchLayer
    - graph::DepthConvertLayer
    - graph::DepthwiseConvolutionLayer
    - graph::DequantizationLayer
    - graph::FlattenLayer
    - graph::QuantizationLayer
    - graph::ReshapeLayer

v17.10 Public maintenance release
 - Bug fixes:
    - Check the maximum local workgroup size supported by OpenCL devices
    - Minor documentation updates (Fixed instructions to build the examples)
    - Introduced a graph::GraphContext
    - Added a few new Graph nodes, support for branches and grouping.
    - Automatically enable cl_printf in debug builds
    - Fixed bare metal builds for armv7a
    - Added AlexNet and cartoon effect examples
    - 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)

v17.09 Public major release
 - Experimental Graph support: initial implementation of a simple stream API to easily chain machine learning layers.
 - Memory Manager (@ref BlobLifetimeManager, @ref BlobMemoryPool, @ref ILifetimeManager, @ref IMemoryGroup, @ref IMemoryManager, @ref IMemoryPool, @ref IPoolManager, @ref MemoryManagerOnDemand, @ref PoolManager)
 - New validation and benchmark frameworks (Boost and Google frameworks replaced by homemade framework).
 - Most machine learning functions support both fixed point 8 and 16 bit (QS8, QS16) for both NEON and OpenCL.
 - New NEON kernels / functions:
    - arm_compute::NEGEMMAssemblyBaseKernel arm_compute::NEGEMMAArch64Kernel
    - @ref NEDequantizationLayerKernel / @ref NEDequantizationLayer
    - @ref NEFloorKernel / @ref NEFloor
    - @ref NEL2NormalizeLayerKernel / @ref NEL2NormalizeLayer
    - @ref NEQuantizationLayerKernel @ref NEMinMaxLayerKernel / @ref NEQuantizationLayer
    - @ref NEROIPoolingLayerKernel / @ref NEROIPoolingLayer
    - @ref NEReductionOperationKernel / @ref NEReductionOperation
    - @ref NEReshapeLayerKernel / @ref NEReshapeLayer

 - New OpenCL kernels / functions:
    - @ref CLDepthwiseConvolutionLayer3x3NCHWKernel @ref CLDepthwiseConvolutionLayer3x3NHWCKernel @ref CLDepthwiseIm2ColKernel @ref CLDepthwiseVectorToTensorKernel @ref CLDepthwiseWeightsReshapeKernel / @ref CLDepthwiseConvolutionLayer3x3 @ref CLDepthwiseConvolutionLayer @ref CLDepthwiseSeparableConvolutionLayer
    - @ref CLDequantizationLayerKernel / @ref CLDequantizationLayer
    - @ref CLDirectConvolutionLayerKernel / @ref CLDirectConvolutionLayer
    - @ref CLFlattenLayer
    - @ref CLFloorKernel / @ref CLFloor
    - @ref CLGEMMTranspose1xW
    - @ref CLGEMMMatrixVectorMultiplyKernel
    - @ref CLL2NormalizeLayerKernel / @ref CLL2NormalizeLayer
    - @ref CLQuantizationLayerKernel @ref CLMinMaxLayerKernel / @ref CLQuantizationLayer
    - @ref CLROIPoolingLayerKernel / @ref CLROIPoolingLayer
    - @ref CLReductionOperationKernel / @ref CLReductionOperation
    - @ref CLReshapeLayerKernel / @ref CLReshapeLayer

v17.06 Public major release
 - Various bug fixes
 - Added support for fixed point 8 bit (QS8) to the various NEON machine learning kernels.
 - Added unit tests and benchmarks (AlexNet, LeNet)
 - Added support for sub tensors.
 - Added infrastructure to provide GPU specific optimisation for some OpenCL kernels.
 - Added @ref OMPScheduler (OpenMP) scheduler for NEON
 - Added @ref SingleThreadScheduler scheduler for NEON (For bare metal)
 - User can specify his own scheduler by implementing the @ref IScheduler interface.
 - New OpenCL kernels / functions:
    - @ref CLBatchNormalizationLayerKernel / @ref CLBatchNormalizationLayer
    - @ref CLDepthConcatenateLayerKernel / @ref CLDepthConcatenateLayer
    - @ref CLHOGOrientationBinningKernel @ref CLHOGBlockNormalizationKernel, @ref CLHOGDetectorKernel / @ref CLHOGDescriptor @ref CLHOGDetector @ref CLHOGGradient @ref CLHOGMultiDetection
    - @ref CLLocallyConnectedMatrixMultiplyKernel / @ref CLLocallyConnectedLayer
    - @ref CLWeightsReshapeKernel / @ref CLConvolutionLayerReshapeWeights
 - New C++ kernels:
    - @ref CPPDetectionWindowNonMaximaSuppressionKernel
 - New NEON kernels / functions:
    - @ref NEBatchNormalizationLayerKernel / @ref NEBatchNormalizationLayer
    - @ref NEDepthConcatenateLayerKernel / @ref NEDepthConcatenateLayer
    - @ref NEDirectConvolutionLayerKernel / @ref NEDirectConvolutionLayer
    - @ref NELocallyConnectedMatrixMultiplyKernel / @ref NELocallyConnectedLayer
    - @ref NEWeightsReshapeKernel / @ref NEConvolutionLayerReshapeWeights

v17.05 Public bug fixes release
 - Various bug fixes
 - Remaining of the functions ported to use accurate padding.
 - Library does not link against OpenCL anymore (It uses dlopen / dlsym at runtime instead to determine whether or not OpenCL is available).
 - Added "free" method to allocator.
 - Minimum version of g++ required for armv7 Linux changed from 4.8 to 4.9

v17.04 Public bug fixes release

 The following functions have been ported to use the new accurate padding:
 -  @ref CLColorConvertKernel
 -  @ref CLEdgeNonMaxSuppressionKernel
 -  @ref CLEdgeTraceKernel
 -  @ref CLGaussianPyramidHorKernel
 -  @ref CLGaussianPyramidVertKernel
 -  @ref CLGradientKernel
 -  @ref NEChannelCombineKernel
 -  @ref NEFillArrayKernel
 -  @ref NEGaussianPyramidHorKernel
 -  @ref NEGaussianPyramidVertKernel
 -  @ref NEHarrisScoreFP16Kernel
 -  @ref NEHarrisScoreKernel
 -  @ref NEHOGDetectorKernel
 -  @ref NELogits1DMaxKernel
 -  NELogits1DShiftExpSumKernel
 -  NELogits1DNormKernel
 -  @ref NENonMaximaSuppression3x3FP16Kernel
 -  @ref NENonMaximaSuppression3x3Kernel

v17.03.1 First Major public release of the sources
 - Renamed the library to arm_compute
 - New CPP target introduced for C++ kernels shared between NEON and CL functions.
 - New padding calculation interface introduced and ported most kernels / functions to use it.
 - New OpenCL kernels / functions:
   - @ref CLGEMMLowpMatrixMultiplyKernel / CLGEMMLowp
 - New NEON kernels / functions:
   - @ref NENormalizationLayerKernel / @ref NENormalizationLayer
   - @ref NETransposeKernel / @ref NETranspose
   - @ref NELogits1DMaxKernel, NELogits1DShiftExpSumKernel, NELogits1DNormKernel / @ref NESoftmaxLayer
   - @ref NEIm2ColKernel, @ref NECol2ImKernel, NEConvolutionLayerWeightsReshapeKernel / @ref NEConvolutionLayer
   - @ref NEGEMMMatrixAccumulateBiasesKernel / @ref NEFullyConnectedLayer
   - @ref NEGEMMLowpMatrixMultiplyKernel / NEGEMMLowp

v17.03 Sources preview
 - New OpenCL kernels / functions:
   - @ref CLGradientKernel, @ref CLEdgeNonMaxSuppressionKernel, @ref CLEdgeTraceKernel / @ref CLCannyEdge
   - GEMM refactoring + FP16 support: @ref CLGEMMInterleave4x4Kernel, @ref CLGEMMTranspose1xWKernel, @ref CLGEMMMatrixMultiplyKernel, @ref CLGEMMMatrixAdditionKernel / @ref CLGEMM
   - @ref CLGEMMMatrixAccumulateBiasesKernel / @ref CLFullyConnectedLayer
   - @ref CLTransposeKernel / @ref CLTranspose
   - @ref CLLKTrackerInitKernel, @ref CLLKTrackerStage0Kernel, @ref CLLKTrackerStage1Kernel, @ref CLLKTrackerFinalizeKernel / @ref CLOpticalFlow
   - @ref CLNormalizationLayerKernel / @ref CLNormalizationLayer
   - @ref CLLaplacianPyramid, @ref CLLaplacianReconstruct
 - New NEON kernels / functions:
   - @ref NEActivationLayerKernel / @ref NEActivationLayer
   - GEMM refactoring + FP16 support (Requires armv8.2 CPU): @ref NEGEMMInterleave4x4Kernel, @ref NEGEMMTranspose1xWKernel, @ref NEGEMMMatrixMultiplyKernel, @ref NEGEMMMatrixAdditionKernel / @ref NEGEMM
   - @ref NEPoolingLayerKernel / @ref NEPoolingLayer

v17.02.1 Sources preview
 - New OpenCL kernels / functions:
   - @ref CLLogits1DMaxKernel, @ref CLLogits1DShiftExpSumKernel, @ref CLLogits1DNormKernel / @ref CLSoftmaxLayer
   - @ref CLPoolingLayerKernel / @ref CLPoolingLayer
   - @ref CLIm2ColKernel, @ref CLCol2ImKernel, CLConvolutionLayerWeightsReshapeKernel / @ref CLConvolutionLayer
   - @ref CLRemapKernel / @ref CLRemap
   - @ref CLGaussianPyramidHorKernel, @ref CLGaussianPyramidVertKernel / @ref CLGaussianPyramid, @ref CLGaussianPyramidHalf, @ref CLGaussianPyramidOrb
   - @ref CLMinMaxKernel, @ref CLMinMaxLocationKernel / @ref CLMinMaxLocation
   - @ref CLNonLinearFilterKernel / @ref CLNonLinearFilter
 - New NEON FP16 kernels (Requires armv8.2 CPU)
   - @ref NEAccumulateWeightedFP16Kernel
   - @ref NEBox3x3FP16Kernel
   - @ref NENonMaximaSuppression3x3FP16Kernel

v17.02 Sources preview
 - New OpenCL kernels / functions:
   - @ref CLActivationLayerKernel / @ref CLActivationLayer
   - @ref CLChannelCombineKernel / @ref CLChannelCombine
   - @ref CLDerivativeKernel / @ref CLChannelExtract
   - @ref CLFastCornersKernel / @ref CLFastCorners
   - @ref CLMeanStdDevKernel / @ref CLMeanStdDev
 - New NEON kernels / functions:
   - HOG / SVM: @ref NEHOGOrientationBinningKernel, @ref NEHOGBlockNormalizationKernel, @ref NEHOGDetectorKernel, NEHOGNonMaximaSuppressionKernel / @ref NEHOGDescriptor, @ref NEHOGDetector, @ref NEHOGGradient, @ref NEHOGMultiDetection
   - @ref NENonLinearFilterKernel / @ref NENonLinearFilter
 - Introduced a CLScheduler to manage the default context and command queue used by the runtime library and create synchronisation events.
 - Switched all the kernels / functions to use tensors instead of images.
 - Updated documentation to include instructions to build the library from sources.

v16.12 Binary preview release
 - Original release

@section S3_how_to_build How to build the library and the examples

@subsection S3_1_build_options Build options

scons 2.3 or above is required to build the library.
To see the build options available simply run ```scons -h```:

	debug: Debug (yes|no)
		default: False
		actual: False

	asserts: Enable asserts (this flag is forced to 1 for debug=1) (yes|no)
		default: False
		actual: False

	arch: Target Architecture (armv7a|arm64-v8a|arm64-v8.2-a|x86_32|x86_64)
		default: armv7a
		actual: armv7a

	os: Target OS (linux|android|bare_metal)
		default: linux
		actual: linux

	build: Build type (native|cross_compile|embed_only)
		default: cross_compile
		actual: cross_compile

	examples: Build example programs (yes|no)
		default: True
		actual: True

	Werror: Enable/disable the -Werror compilation flag (yes|no)
		default: True
		actual: True

	opencl: Enable OpenCL support (yes|no)
		default: True
		actual: True

	neon: Enable Neon support (yes|no)
		default: False
		actual: False

	gles_compute: Enable OpenGL ES Compute Shader support (yes|no)
		default: False
		actual: False

	embed_kernels: Embed OpenCL kernels and OpenGL ES compute shader in library binary (yes|no)
		default: True
		actual: True

	set_soname: Set the library's soname and shlibversion (requires SCons 2.4 or above) (yes|no)
		default: False
		actual: False

	openmp: Enable OpenMP backend (yes|no)
		default: False
		actual: False

	cppthreads: Enable C++11 threads backend (yes|no)
		default: True
		actual: True

	build_dir: Specify sub-folder for the build ( /path/to/build_dir )
		default: .
		actual: .

	extra_cxx_flags: Extra CXX flags to be appended to the build command
		default:
		actual:

	pmu: Enable PMU counters (yes|no)
		default: False
		actual: False

	mali: Enable Mali hardware counters (yes|no)
		default: False
		actual: False

	validation_tests: Build validation test programs (yes|no)
		default: False
		actual: False

	benchmark_tests: Build benchmark test programs (yes|no)
		default: False
		actual: False

@b debug / @b asserts:
 - With debug=1 asserts are enabled, and the library is built with symbols and no optimisations enabled.
 - 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)
 - 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).

@b arch: The x86_32 and x86_64 targets can only be used with neon=0 and opencl=1.

@b os: Choose the operating system you are targeting: Linux, Android or bare metal.
@note bare metal can only be used for NEON (not OpenCL), only static libraries get built and NEON's multi-threading support is disabled.

@b build: 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.

@note 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.

There is also an 'embed_only' option which will generate all the .embed files for the OpenCL kernels and / or OpenGLES compute shaders. This might be useful if using a different build system to compile the library.

@b Werror: 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 developer@arm.com so that the issue can be addressed).

@b opencl / @b neon / @b gles_compute: Choose which SIMD technology you want to target. (NEON for ARM Cortex-A CPUs or OpenCL / GLES_COMPUTE for ARM Mali GPUs)

@b embed_kernels: 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".

@b set_soname: Do you want to build the versioned version of the library ?

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 -> libarm_compute_core.so.1.0.0
  libarm_compute_core.so.1 -> libarm_compute_core.so.1.0.0
  libarm_compute_core.so.1.0.0

@note This options is disabled by default as it requires SCons version 2.4 or above.

@b extra_cxx_flags: Custom CXX flags which will be appended to the end of the build command.

@b build_dir: Build the library in a subfolder of the "build" folder. (Allows to build several configurations in parallel).

@b examples: Build or not the examples

@b validation_tests: Enable the build of the validation suite.

@b benchmark_tests: Enable the build of the benchmark tests

@b pmu: Enable the PMU cycle counter to measure execution time in benchmark tests. (Your device needs to support it)

@b mali: 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)

@b openmp Build in the OpenMP scheduler for NEON.

@note Only works when building with g++ not clang++

@b cppthreads Build in the C++11 scheduler for NEON.

@sa Scheduler::set

@subsection S3_2_linux Building for Linux

@subsubsection S3_2_1_library How to build the library ?

For Linux, the library was successfully built and tested using the following Linaro GCC toolchain:

 - gcc-linaro-arm-linux-gnueabihf-4.9-2014.07_linux
 - gcc-linaro-4.9-2016.02-x86_64_aarch64-linux-gnu
 - gcc-linaro-6.3.1-2017.02-i686_aarch64-linux-gnu

To cross-compile the library in debug mode, with NEON only support, for Linux 32bit:

	scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=linux arch=armv7a

To cross-compile the library in asserts mode, with OpenCL only support, for Linux 64bit:

	scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=linux arch=arm64-v8a

To cross-compile the library in asserts mode, with GLES_COMPUTE only support, for Linux 64bit:

	scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=0 gles_compute=1 embed_kernels=1 os=linux arch=arm64-v8a

You can also compile the library natively on an ARM device by using <b>build=native</b>:

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=arm64-v8a build=native
	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=native

@note 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.

For example on a 64bit Debian based system you would have to install <b>g++-arm-linux-gnueabihf</b>

	apt-get install g++-arm-linux-gnueabihf

Then run

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=cross_compile

or simply remove the build parameter as build=cross_compile is the default value:

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a

@attention To cross compile with opencl=1 you need to make sure to have a version of libOpenCL matching your target architecture.

@subsubsection S3_2_2_examples How to manually build the examples ?

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.

@note The following command lines assume the arm_compute 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.

To cross compile a NEON example for Linux 32bit:

	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

To cross compile a NEON example for Linux 64bit:

	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

(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)

To cross compile an OpenCL example for Linux 32bit:

	arm-linux-gnueabihf-g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -L. -larm_compute -larm_compute_core -o cl_convolution -DARM_COMPUTE_CL

To cross compile an OpenCL example for Linux 64bit:

	aarch64-linux-gnu-g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -L. -larm_compute -larm_compute_core -o cl_convolution -DARM_COMPUTE_CL

To cross compile a GLES example for Linux 32bit:

	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

To cross compile a GLES example for Linux 64bit:

	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

(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)

To cross compile the examples with the Graph API, such as graph_lenet.cpp, you need to link the examples against arm_compute_graph.so too.

@note The compute library must currently be built with both neon and opencl enabled - neon=1 and opencl=1

i.e. to cross compile the "graph_lenet" example for Linux 32bit:

	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

i.e. to cross compile the "graph_lenet" example for Linux 64bit:

	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

(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)

@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute, arm_compute_core

To compile natively (i.e directly on an ARM device) for NEON for Linux 32bit:

	g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -mfpu=neon -larm_compute -larm_compute_core -o neon_convolution

To compile natively (i.e directly on an ARM device) for NEON for Linux 64bit:

	g++ examples/neon_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute -larm_compute_core -o neon_convolution

(notice the only difference with the 32 bit command is that we don't need the -mfpu option)

To compile natively (i.e directly on an ARM device) for OpenCL for Linux 32bit or Linux 64bit:

	g++ examples/cl_convolution.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute -larm_compute_core -o cl_convolution -DARM_COMPUTE_CL

To compile natively (i.e directly on an ARM device) for GLES for Linux 32bit or Linux 64bit:

	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

To compile natively the examples with the Graph API, such as graph_lenet.cpp, you need to link the examples against arm_compute_graph.so too.
@note The compute library must currently be built with both neon and opencl enabled - neon=1 and opencl=1

i.e. to natively compile the "graph_lenet" example for Linux 32bit:

	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

i.e. to natively compile the "graph_lenet" example for Linux 64bit:

	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

(notice the only difference with the 32 bit command is that we don't need the -mfpu option)

@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute, arm_compute_core

@note These two commands assume libarm_compute.so is available in your library path, if not add the path to it using -L

To run the built executable simply run:

	LD_LIBRARY_PATH=build ./neon_convolution

or

	LD_LIBRARY_PATH=build ./cl_convolution

@note Examples accept different types of arguments, to find out what they are run the example without any argument and the help will be displayed at the beginning of the run.

For example:
	LD_LIBRARY_PATH=. ./graph_lenet

	./graph_lenet

	Usage: ./graph_lenet [target] [path_to_data] [batches]

	No data folder provided: using random values

	Test passed

In this case the first argument of LeNet (like all the graph examples) is the target (i.e 0 to run on NEON, 1 to run on OpenCL if available, 2 to run on OpenCL using the CLTuner), the second argument is the path to the folder containing the npy files for the weights and finally the third argument is the number of batches to run.

@subsection S3_3_android Building for Android

For Android, the library was successfully built and tested using Google's standalone toolchains:
 - clang++ from NDK r16b for armv7a
 - clang++ from NDK r16b for arm64-v8a

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>

- Download the NDK r16b from here: https://developer.android.com/ndk/downloads/index.html
- Make sure you have Python 2 installed on your machine.
- Generate the 32 and/or 64 toolchains by running the following commands:

	$NDK/build/tools/make_standalone_toolchain.py --arch arm64 --install-dir $MY_TOOLCHAINS/aarch64-linux-android-ndk-r16b --stl gnustl --api 21
	$NDK/build/tools/make_standalone_toolchain.py --arch arm --install-dir $MY_TOOLCHAINS/arm-linux-android-ndk-r16b --stl gnustl --api 21

@attention Due to some NDK issues make sure you use clang++ & gnustl

@note 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

@subsubsection S3_3_1_library How to build the library ?

To cross-compile the library in debug mode, with NEON only support, for Android 32bit:

	CXX=clang++ CC=clang scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=android arch=armv7a

To cross-compile the library in asserts mode, with OpenCL only support, for Android 64bit:

	CXX=clang++ CC=clang scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=android arch=arm64-v8a

To cross-compile the library in asserts mode, with GLES_COMPUTE only support, for Android 64bit:

	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

@subsubsection S3_3_2_examples How to manually build the examples ?

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.

@note The following command lines assume the arm_compute 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.

Once you've got your Android standalone toolchain built and added to your path you can do the following:

To cross compile a NEON example:

	#32 bit:
	arm-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
	#64 bit:
	aarch64-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

To cross compile an OpenCL example:

	#32 bit:
	arm-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 -DARM_COMPUTE_CL
	#64 bit:
	aarch64-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 -DARM_COMPUTE_CL

To cross compile a GLES example:

	#32 bit:
	arm-linux-androideabi-clang++ examples/gc_absdiff.cpp utils/Utils.cpp -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++ examples/gc_absdiff.cpp utils/Utils.cpp -I. -Iinclude -std=c++11 -larm_compute-static -larm_compute_core-static -L. -o gc_absdiff_aarch64 -static-libstdc++ -pie -DARM_COMPUTE_GC

To cross compile the examples with the Graph API, such as graph_lenet.cpp, 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)

	#32 bit:
	arm-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 -DARM_COMPUTE_CL
	#64 bit:
	aarch64-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 -DARM_COMPUTE_CL

@note Due to some issues in older versions of the Mali OpenCL DDK (<= r13p0), we recommend to link arm_compute statically on Android.
@note When linked statically the arm_compute_graph library currently needs the --whole-archive linker flag in order to work properly

Then you need to do is upload the executable and the shared library to the device using ADB:

	adb push neon_convolution_arm /data/local/tmp/
	adb push cl_convolution_arm /data/local/tmp/
	adb push gc_absdiff_arm /data/local/tmp/
	adb shell chmod 777 -R /data/local/tmp/

And finally to run the example:

	adb shell /data/local/tmp/neon_convolution_arm
	adb shell /data/local/tmp/cl_convolution_arm
	adb shell /data/local/tmp/gc_absdiff_arm

For 64bit:

	adb push neon_convolution_aarch64 /data/local/tmp/
	adb push cl_convolution_aarch64 /data/local/tmp/
	adb push gc_absdiff_aarch64 /data/local/tmp/
	adb shell chmod 777 -R /data/local/tmp/

And finally to run the example:

	adb shell /data/local/tmp/neon_convolution_aarch64
	adb shell /data/local/tmp/cl_convolution_aarch64
	adb shell /data/local/tmp/gc_absdiff_aarch64

@note Examples accept different types of arguments, to find out what they are run the example without any argument and the help will be displayed at the beginning of the run.

For example:
	adb shell /data/local/tmp/graph_lenet

	/data/local/tmp/graph_lenet

	Usage: /data/local/tmp/graph_lenet [target] [path_to_data] [batches]

	No data folder provided: using random values

	Test passed

In this case the first argument of LeNet (like all the graph examples) is the target (i.e 0 to run on NEON, 1 to run on OpenCL if available, 2 to run on OpenCL using the CLTuner), the second argument is the path to the folder containing the npy files for the weights and finally the third argument is the number of batches to run.

@subsection S3_4_bare_metal Building for bare metal

For bare metal, the library was successfully built using linaros's latest (gcc-linaro-6.3.1-2017.05) bare metal toolchains:
 - arm-eabi for armv7a
 - aarch64-elf for arm64-v8a

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>.

@note 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

@subsubsection S3_4_1_library How to build the library ?

To cross-compile the library with NEON support for baremetal arm64-v8a:

	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

@subsubsection S3_4_2_examples How to manually build the examples ?

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>.

@subsection S3_5_windows_host Building on a Windows host system

Using `scons` directly from the Windows command line is known to cause
problems. The reason seems to be that if `scons` 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.

@subsubsection S3_5_1_ubuntu_on_windows Bash on Ubuntu on Windows

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 *beta* and thus might not be available.
However, if it is building the library is as simple as opening a *Bash on
Ubuntu on Windows* shell and following the general guidelines given above.

@subsubsection S3_5_2_cygwin Cygwin

If the Windows subsystem for Linux is not available <a href="https://www.cygwin.com/">Cygwin</a> 
can be used to install and run `scons`. In addition to the default packages
installed by Cygwin `scons` has to be selected in the installer. (`git` 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.

@subsection S3_6_cl_stub_library The OpenCL stub library

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 arm_compute against.

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.

@warning This OpenCL library provided is a stub and *not* a real implementation. You can use it to resolve OpenCL's symbols in arm_compute 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.

To cross-compile the stub OpenCL library simply run:

	<target-prefix>-gcc -o libOpenCL.so -Iinclude opencl-1.2-stubs/opencl_stubs.c -fPIC -shared

For example:

	#Linux 32bit
	arm-linux-gnueabihf-gcc -o libOpenCL.so -Iinclude opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
	#Linux 64bit
	aarch64-linux-gnu-gcc -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC
	#Android 32bit
	arm-linux-androideabi-clang -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC -shared
	#Android 64bit
	aarch64-linux-android-clang -o libOpenCL.so -Iinclude -shared opencl-1.2-stubs/opencl_stubs.c -fPIC -shared

@subsection S3_7_gles_stub_library The Linux OpenGLES and EGL stub libraries

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 arm_compute against.

@note The stub libraries are only needed on Linux. For Android, the NDK toolchains already provide the meta-EGL and meta-GLES libraries.

To cross-compile the stub OpenGLES and EGL libraries simply run:

	<target-prefix>-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
	<target-prefix>-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared

	#Linux 32bit
	arm-linux-gnueabihf-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
	arm-linux-gnueabihf-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared

	#Linux 64bit
	aarch64-linux-gnu-gcc -o libEGL.so -Iinclude/linux opengles-3.1-stubs/EGL.c -fPIC -shared
	aarch64-linux-gnu-gcc -o libGLESv2.so -Iinclude/linux opengles-3.1-stubs/GLESv2.c -fPIC -shared
*/
} // namespace arm_compute