///
/// Copyright (c) 2017-2024 Arm Limited.
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namespace arm_compute
{
/** @page versions_changelogs Release Versions and Changelog

@tableofcontents

@section 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.
@note Starting from release 22.05, 'master' branch is no longer being used, it has been replaced by 'main'. Please update your clone jobs accordingly.

@section S2_2_changelog Changelog

@warning After v24.08 release, changelog associated with each release can be found in https://github.com/ARM-software/ComputeLibrary/releases. They won't be included in this file.

v24.08 Public major release
 - Expose CpuAdd functionality using the experimental operators api
 - Expose CpuDepthwiseConv2d functionality using the experimental operators api
 - Expose CpuElementwiseDivision functionality using the experimental operators api
 - Expose CpuElementwiseMax functionality using the experimental operators api
 - Expose CpuElementwiseMin functionality using the experimental operators api
 - Expose CpuGemmAssemblyDispatch functionality using the experimental operators low-level api
 - Expose CpuMul functionality using the experimental operators api
 - Expose CpuSub functionality using the experimental operators api

v24.07 Public major release
 - Expose CpuActivation functionality using the experimental operators api
 - Expose CpuGemm functionality using the experimental operators api
 - Expose CpuGemmConv2d functionality using the experimental operators api
 - Expose CpuGemmDirectConv2d functionality using the experimental operators api
 - Expose CpuTranspose functionality using the experimental operators api
 - Expose CpuWinogradConv2d functionality using the experimental operators api
 - Optimize CPU operator memory management.
 - Add support for mixed sign quantized convolution.
 - Add support for mixed sign dequantized GEMM.
 - Add SME FP32 kernel for logistic activation function
 - Add SME FP16 GEMV kernel.
 - Change SME vector length function to use RDSVL instead of static variable.
 - Remove unused "get_default_activation_values" functions.
 - Add SVE fixed format interleaved BF16 DOT kernel.
 - Updates and optimizations to assembly kernels.
 - Add Optimized SME kernel for QASYMM8_SIGNED elementwise multiplication operation

v24.06 Public minor release
 - Enable FP16 in multiple Neon™ kernels for multi_isa + v8a
 - Fix OpenMP® thread scheduling for large machine
 - Optimize CPU activation functions using LUT-based implementation:
   - Tanh function for FP16.

v24.05 Public major release
 - Add @ref CLScatter operator for FP32/16, S32/16/8, U32/16/8 data types
 - Various fixes to enable FP16 kernels in armv8a multi_isa builds.
 - Updated logic in the OpenMP scheduler to exclude LITTLE cores.

v24.04 Public major release
 - Add Bfloat16 data type support for @ref NEMatMul.
 - Add support for SoftMax in SME2 for FP32, FP16, QASYMM8 and QASYMM8_SIGNED.
 - Add support for in place accumulation to CPU GEMM kernels.
 - Add low-precision Int8 * Int8 -> FP32 CPU GEMM which dequantizes after multiplication
 - Add is_dynamic flag to QuantizationInfo to signal to operators that it may change after configuration
 - Performance optimizations:
   - Optimize start-up time of @ref NEConvolutionLayer for some input configurations where GeMM is selected as the convolution algorithm
   - Optimize @ref NEConvolutionLayer for input tensor size > 1e7 bytes and weight tensor height > 7
   - Optimize @ref NESoftmaxLayer for axis != 0 by natively supporting higher axes up to axis 3.

v24.02.1 Public patch release
 - Fix performance regression in fixed-format kernels
 - Fix compile and runtime errors in arm_compute_validation for Windows on Arm(WoA)

v24.02 Public major release
 - Replace template writer with compute kernel writer in dynamic fusion.
 - Performance optimizations:
   - Parallelize @ref NEDepthwiseConvolutionLayer over batches if there is only 1 row

v24.01 Public major release
 - Remove the legacy 'libarm_compute_core' library. This library is an artifact of Compute Library's legacy library architecture and no longer serves any purpose.
  You should link only to the main `libarm_compute` library for core functionality.
 - Expand GPUTarget list with Mali™ G720 and G620.
 - Optimize CPU activation functions using LUT-based implementation:
   - Sigmoid function for FP16.
 - New features
   - Add support for FP16 in all multi_isa builds.
 - Performance optimizations:
   - Optimize @ref NESoftmaxLayer
   - Optimize @ref NEDepthToSpaceLayer.

v23.11 Public major release
 - New features
   - Add support for input data type U64/S64 in CLCast and NECast.
   - Add support for output data type S64 in NEArgMinMaxLayer and CLArgMinMaxLayer
   - Port the following kernels in the experimental Dynamic Fusion interface to use the new Compute Kernel Writer interface:
     - experimental::dynamic_fusion::GpuCkwResize
     - experimental::dynamic_fusion::GpuCkwPool2d
     - experimental::dynamic_fusion::GpuCkwDepthwiseConv2d
     - experimental::dynamic_fusion::GpuCkwMatMul
   - Add support for OpenCL™ comand buffer with mutable dispatch extension.
   - Add support for Arm® Cortex®-A520 and Arm® Cortex®-R82.
   - Add support for negative axis values and inverted axis values in @ref arm_compute::NEReverse and @ref arm_compute::CLReverse.
   - Add new OpenCL™ kernels:
     - opencl::kernels::ClMatMulLowpNativeMMULKernel support for QASYMM8 and QASYMM8_SIGNED, with batch support
 - Performance optimizations:
   - Optimize cpu::CpuReshape
   - Optimize opencl::ClTranspose
   - Optimize @ref NEStackLayer
   - Optimize @ref CLReductionOperation.
   - Optimize @ref CLSoftmaxLayer.
   - Optimize start-up time of @ref NEConvolutionLayer for some input configurations where GeMM is selected as the convolution algorithm
   - Reduce CPU Overhead by optimal flushing of CL kernels.
 - Deprecate support for Bfloat16 in cpu::CpuCast.
 - Support for U32 axis in @ref arm_compute::NEReverse and @ref arm_compute::CLReverse will be deprecated in 24.02.
 - Remove legacy PostOps interface. PostOps was the experimental interface for kernel fusion and is replaced by the new Dynamic Fusion interface.
 - Update OpenCL™ API headers to v2023.04.17

v23.08 Public major release
 - Deprecate the legacy 'libarm_compute_core' library. This library is an artifact of Compute Library's legacy library architecture and no longer serves any purpose.
 Users must no longer link their applications to this library and instead link only to the main `libarm_compute` library for core functionality.
 - New features
   - Rewrite CLArgMinMaxLayer for axis 0 and enable S64 output.
   - Add multi-sketch support for dynamic fusion.
   - Break up arm_compute/core/Types.h and utils/Utils.h a bit to reduce unused code in each inclusion of these headers.
   - Add Fused Activation to CLMatMul.
   - Implement FP32/FP16 opencl::kernels::ClMatMulNativeMMULKernel using the MMUL extension.
   - Use MatMul in fully connected layer with dynamic weights when supported.
   - Optimize CPU depthwise convolution with channel multiplier.
   - Add support in CpuCastKernel for conversion of S64/U64 to F32.
   - Add new OpenCL™ kernels:
     - opencl::kernels::ClMatMulNativeMMULKernel support for FP32 and FP16, with batch support
   - Enable transposed convolution with non-square kernels on CPU and GPU.
   - Add support for input data type U64/S64 in CLCast.
   - Add new Compute Kernel Writer (CKW) subproject that offers a C++ interface to generate tile-based OpenCL code in just-in-time fashion.
   - Port the following kernels in the experimental Dynamic Fusion interface to use the new Compute Kernel Writer interface with support for FP16/FP32 only:
     - experimental::dynamic_fusion::GpuCkwActivation
     - experimental::dynamic_fusion::GpuCkwCast
     - experimental::dynamic_fusion::GpuCkwDirectConv2d
     - experimental::dynamic_fusion::GpuCkwElementwiseBinary
     - experimental::dynamic_fusion::GpuCkwStore
 - Various optimizations and bug fixes.

v23.05.1 Public patch release
 - Enable CMake and Bazel option to build multi_isa without FP16 support.
 - Fix compilation error in NEReorderLayer (aarch64 only).
 - Disable invalid (false-negative) validation test with CPU scale layer on FP16.
 - Various bug fixes

v23.05 Public major release
 - New features:
   - Add new Arm® Neon™ kernels / functions:
      - @ref NEMatMul for QASYMM8, QASYMM8_SIGNED, FP32 and FP16, with batch support.
      - NEReorderLayer (aarch64 only)
   - Add new OpenCL™ kernels / functions:
      - @ref CLMatMul support for QASYMM8, QASYMM8_SIGNED, FP32 and FP16, with batch support.
   - Add support for the multiple dimensions in the indices parameter for both the Arm® Neon™ and OpenCL™ implementations of the Gather Layer.
   - Add support for dynamic weights in @ref CLFullyConnectedLayer and @ref NEFullyConnectedLayer for all data types.
   - Add support for cropping in the Arm® Neon™ and OpenCL™: implementations of the BatchToSpace Layer for all data types.
   - Add support for quantized data types for the ElementwiseUnary Operators for Arm® Neon™.
   - Implement RSQRT for quantized data types on OpenCL™.
   - Add FP16 depthwise convolution kernels for SME2.
 - Performance optimizations:
   - Improve CLTuner exhaustive mode tuning time.
 - Deprecate dynamic block shape in @ref NEBatchToSpaceLayer and @ref CLBatchToSpaceLayer.
 - Various optimizations and bug fixes.

v23.02.1 Public patch release
 - Allow mismatching data layouts between the source tensor and weights for cpu::CpuGemmDirectConv2d CpuGemmDirectConv2d with fixed format kernels.
 - Fixes for experimental CPU only Bazel and CMake builds.

v23.02 Public major release
 - New features:
   - Rework the experimental dynamic fusion interface by identifying auxiliary and intermediate tensors, and specifying an explicit output operator.
   - Add the following operators to the experimental dynamic fusion API:
     - GpuAdd, GpuCast, GpuClamp, GpuDepthwiseConv2d, GpuMul, GpuOutput, GpuPool2d, GpuReshape, GpuResize, GpuSoftmax, GpuSub.
   - Add SME/SME2 kernels for GeMM, Winograd convolution, Depthwise convolution and Pooling.
   - Add new CPU operator AddMulAdd for float and quantized types.
   - Add new flag @ref ITensorInfo::lock_paddings() to tensors to prevent extending tensor paddings.
   - Add experimental support for CPU only Bazel and CMake builds.
 - Performance optimizations:
   - Optimize CPU base-e exponential functions for FP32.
   - Optimize CPU StridedSlice by copying first dimension elements in bulk where possible.
   - Optimize CPU quantized Subtraction by reusing the quantized Addition kernel.
   - Optimize CPU ReduceMean by removing quantization steps and performing the operation in integer domain.
   - Optimize GPU Scale and Dynamic Fusion GpuResize by removing quantization steps and performing the operation in integer domain.
   - Update the heuristic for CLDepthwiseConvolutionNative kernel.
   - Add new optimized OpenCL kernel to compute indirect convolution:
     - opencl::kernels::ClIndirectConv2dKernel ClIndirectConv2dKernel
   - Add new optimized OpenCL kernel to compute transposed convolution:
     - opencl::kernels::ClTransposedConvolutionKernel ClTransposedConvolutionKernel
 - Update recommended/minimum NDK version to r20b.
 - Various optimizations and bug fixes.

v22.11 Public major release
 - New features:
   - Add new experimental dynamic fusion API.
   - Add CPU batch matrix multiplication with adj_x = false and adj_y = false for FP32.
   - Add CPU MeanStdDevNorm for QASYMM8.
   - Add CPU and GPU GELU activation function for FP32 and FP16.
   - Add CPU swish activation function for FP32 and FP16.
 - Performance optimizations:
   - Optimize CPU bilinear scale for FP32, FP16, QASYMM8, QASYMM8_SIGNED, U8 and S8.
   - Optimize CPU activation functions using LUT-based implementation:
     - Sigmoid function for QASYMM8 and QASYMM8_SIGNED.
     - Hard swish function for QASYMM8_SIGNED.
   - Optimize CPU addition for QASYMM8 and QASYMM8_SIGNED using fixed-point arithmetic.
   - Optimize CPU multiplication, subtraction and activation layers by considering tensors as 1D.
   - Optimize GPU depthwise convolution kernel and heuristic.
   - Optimize GPU Conv2d heuristic.
   - Optimize CPU MeanStdDevNorm for FP16.
   - Optimize CPU tanh activation function for FP16 using rational approximation.
 - Improve GPU GeMMLowp start-up time.
 - Various optimizations and bug fixes.

v22.08 Public major release
 - Various bug fixes.
 - Disable unsafe FP optimizations causing accuracy issues in:
   - opencl::kernels::ClDirectConv2dKernel ClDirectConv2dKernel
   - opencl::kernels::ClDirectConv2dKernel ClDirectConv3dKernel
   - CLDepthwiseConvolutionLayerNativeKernel
 - Add Dynamic Fusion of Elementwise Operators: Div, Floor, Add.
 - Optimize the gemm_reshaped_rhs_nly_nt OpenCL kernel using the arm_matrix_multiply extension available for Arm® Mali™-G715 and Arm® Mali™-G615.
 - Add support for the arm_matrix_multiply extension in the gemmlowp_mm_reshaped_only_rhs_t OpenCL kernel.
 - Expand GPUTarget list with missing Mali™ GPUs product names: G57, G68, G78AE, G610, G510, G310.
 - Extend the direct convolution 2d interface to configure the block size.
 - Update ClConv2D heuristic to use direct convolution.
 - Use official Khronos® OpenCL extensions:
   - Add cl_khr_integer_dot_product extension support.
   - Add support of OpenCL 3.0 non-uniform workgroup.
 - Cpu performance optimizations:
   - Add LUT-based implementation of Hard Swish and Leaky ReLU activation function for aarch64 build.
   - Optimize Add layer by considering the input tensors as 1D array.
 - Add fixed-format BF16, FP16 and FP32 Neon™ GEMM kernels to support variable weights.
 - Add new winograd convolution kernels implementation and update the ACL arm_compute::cpu::CpuWinogradConv2d CpuWinogradConv2d operator.
 - Add experimental support for native builds for Windows® on Arm™.
 - Build flag interpretation change: arch=armv8.6-a now translates to -march=armv8.6-a CXX flag instead of march=armv8.2-a + explicit selection of feature extensions.
 - Build flag change: toolchain_prefix, compiler_prefix:
   - Use empty string "" to suppress any prefixes.
   - Use "auto" to use default (auto) prefixes chosen by the build script. This is the default behavior when unspecified.
   - Any other string will be used as custom prefixes to the compiler and the rest of toolchain tools.
   - The default behaviour when prefix is unspecified does not change, but its signifier has been changed from empty string "" to "auto".
 - armv7a with Android build will no longer be tested or maintained.

v22.05 Public major release
 - Various bug fixes.
 - Various optimizations.
 - Add support for NDK r23b.
 - Inclusive language adjustment. Please refer to @ref S5_0_inc_lang for details.
 - New Arm® Neon™ kernels / functions :
   - opencl::kernels::ClPool3dKernel ClPool3dKernel
 - New OpenCL kernels / functions :
   - cpu::kernels::CpuPool3dKernel CpuPool3dKernel
 - Improve the start-up times for the following OpenCL kernels:
   - opencl::kernels::ClWinogradInputTransformKernel ClWinogradInputTransformKernel
   - opencl::kernels::ClWinogradOutputTransformKernel ClWinogradOutputTransformKernel
   - opencl::kernels::ClWinogradFilterTransformKernel ClWinogradFilterTransformKernel
   - opencl::kernels::ClHeightConcatenateKernel ClHeightConcatenateKernel
 - Decouple the implementation of the following Cpu kernels into various data types (fp32, fp16, int):
   - cpu::kernels::CpuDirectConv2dKernel CpuDirectConv2dKernel
   - cpu::kernels::CpuDepthwiseConv2dNativeKernel CpuDepthwiseConv2dNativeKernel
   - cpu::kernels::CpuGemmMatrixAdditionKernel CpuGemmMatrixAdditionKernel
   - cpu::kernels::CpuGemmMatrixMultiplyKernel CpuGemmMatrixMultiplyKernel
   - NEFuseBatchNormalizationKernel
   - NEL2NormalizeLayerKernel

v22.02 Public major release
 - Various bug fixes.
 - Various optimizations.
 - Update A510 arm_gemm cpu Kernels.
 - Inclusive language adjustment. Please refer to @ref S5_0_inc_lang for details.
 - Improve the start-up time for the following OpenCL kernels:
   - @ref CLScale
   - @ref CLGEMM
   - @ref CLDepthwiseConvolutionLayer
   - opencl::kernels::ClIm2ColKernel ClIm2ColKernel
   - opencl::kernels::ClDirectConv2dKernel ClDirectConv2dKernel
 - Remove functions:
   - CLRemap
   - NERemap
 - Remove padding from OpenCL kernels:
   - opencl::kernels::ClDirectConv2dKernel ClDirectConv2dKernel
 - Remove padding from Cpu kernels:
   - cpu::kernels::CpuDirectConv2dKernel CpuDirectConv2dKernel
 - Decouple the implementation of the following Cpu kernels into various data types (fp32, fp16, int):
   - cpu::kernels::CpuActivationKernel CpuActivationKernel
   - cpu::kernels::CpuAddKernel CpuAddKernel
   - cpu::kernels::CpuElementwiseKernel CpuElementwiseKernel
   - cpu::CpuSoftmaxGeneric CpuSoftmaxKernel
   - NEBoundingBoxTransformKernel
   - NECropKernel
   - NEComputeAllAnchorsKernel
   - NEInstanceNormalizationLayerKernel
   - NEMaxUnpoolingLayerKernel
   - NEMeanStdDevNormalizationKernel
   - NERangeKernel
   - NEROIAlignLayerKernel
   - NESelectKernel

v21.11 Public major release
 - Various bug fixes.
 - Various optimizations:
   - Improve performance of bilinear and nearest neighbor Scale on both CPU and GPU for FP32, FP16, Int8, Uint8 data types
   - Improve performance of Softmax on GPU for Uint8/Int8
 - New OpenCL kernels / functions:
   - @ref CLConv3D
 - New Arm® Neon™ kernels / functions:
   - @ref NEConv3D
 - Support configurable build by a selected subset of operator list
 - Support MobileBert on Neon™ backend
 - Improve operator/function logging
 - Remove padding from OpenCL kernels:
   - ClPool2dKernel
   - ClScaleKernel
   - ClGemmMatrixMultiplyReshapedKernel
 - Remove padding from Cpu kernels:
   - CpuPool2dKernel
 - Remove Y padding from OpenCL kernels:
   - ClGemmMatrixMultiplyKernel
   - ClGemmReshapedRHSMatrixKernel
 - Remove legacy GeMM kernels in gemm_v1.cl

v21.08 Public major release
 - Various bug fixes.
 - Various optimizations:
  - Improve LWS (Local-Workgroup-Size) heuristic in OpenCL for GeMM, Direct Convolution and Winograd Transformations when OpenCL tuner is not used
  - Improve QASYMM8/QSYMM8 performance on OpenCL for various Arm® Mali™ GPU architectures
  - Add dynamic weights support in Fully connected layer (CPU/GPU)
  - Various performance optimizations for floating-point data types (CPU/GPU)
 - Add a reduced core library build arm_compute_core_v2
 - Expose Operator API
 - Support fat binary build for arm8.2-a via fat_binary build flag
 - Add CPU discovery capabilities
 - Add data type f16 support for:
  - CLRemapKernel
 - Port the following functions to stateless API:
   - @ref CLConvolutionLayer
   - @ref CLFlattenLayer
   - @ref CLFullyConnectedLayer
   - @ref CLGEMM
   - @ref CLGEMMConvolutionLayer
   - @ref CLGEMMLowpMatrixMultiplyCore
   - @ref CLWinogradConvolutionLayer
   - @ref NEConvolutionLayer
   - @ref NEFlattenLayer
   - @ref NEFullyConnectedLayer
   - @ref NEGEMM
   - @ref NEGEMMConv2d
   - @ref NEGEMMConvolutionLayer
   - @ref NEGEMMLowpMatrixMultiplyCore
   - @ref NEWinogradConvolutionLayer
 - Remove the following functions:
   - CLWinogradInputTransform
 - Remove CLCoreRuntimeContext
 - Remove ICPPSimpleKernel
 - Rename file arm_compute/runtime/CL/functions/CLElementWiseUnaryLayer.h to arm_compute/runtime/CL/functions/CLElementwiseUnaryLayer.h

v21.05 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Various documentation updates:
   - Add supported operators and corresponding Android NNAPI operators.
   - Documentation reorg into user guide and contributor guide.
 - Add support for a global allocator for OpenCL tensors
 - Add experimental support for [CLVK](https://github.com/kpet/clvk).
 - Add data type S32 support for:
  - opencl::kernels::ClArithmeticKernel
 - Add data type QASYMM8 support for:
  - @ref CLROIPoolingLayer
  - CLROIPoolingLayerKernel
  - @ref NEROIPoolingLayer
  - NEROIPoolingLayerKernel
 - Add per-channel quantization support for:
  - @ref CLDeconvolutionLayer
  - @ref CLDirectDeconvolutionLayer
  - @ref NEConvolutionLayer
  - @ref NEDeconvolutionLayer
 - Remove padding from OpenCL kernels:
   - CLL2NormalizeLayerKernel
   - CLDepthwiseConvolutionLayer3x3NHWCKernel
   - CLNormalizationLayerKernel
   - CLNormalizePlanarYUVLayerKernel
   - opencl::kernels::ClMulKernel
   - CLReductionOperationKernel
   - CLROIPoolingLayerKernel
 - Remove computer vision support from Arm® Neon™ backend
 - Remove the following functions:
   - NEAbsoluteDifference
   - NEAccumulate
   - NEBox3x3
   - NECannyEdge
   - NEChannelCombine
   - NEChannelExtract
   - NEColorConvert
   - NEConvolution
   - NEDerivative
   - NEDilate
   - NEEqualizeHistogram
   - NEErode
   - NEFastCorners
   - NEGaussian3x3
   - NEGaussian5x5
   - NEGaussianPyramid
   - NEHOGDescriptor
   - NEHOGDetector
   - NEHOGGradient
   - NEHOGMultiDetection
   - NEHarrisCorners
   - NEHistogram
   - NEIntegralImage
   - NELaplacianPyramid
   - NELaplacianReconstruct
   - NEMagnitude
   - NEMeanStdDev
   - NEMedian3x3
   - NEMinMaxLocation
   - NENonLinearFilter
   - NEOpticalFlow
   - NEPhase
   - NEScharr3x3
   - NESobel3x3
   - NESobel5x5
   - NESobel7x7
   - NETableLookup
   - NEThreshold
   - NEWarpAffine
   - NEWarpPerspectiveKernel
 - Remove all GLES kernels / functions / tests / examples
 - Remove computer vision support from CL backend
 - Remove the following functions:
   - CLAbsoluteDifference
   - CLAccumulate
   - CLBox3x3
   - CLCannyEdge
   - CLChannelCombine
   - CLChannelExtract
   - CLColorConvert
   - CLConvolution
   - CLDerivative
   - CLDilate
   - CLEqualizeHistogram
   - CLErode
   - CLFastCorners
   - CLGaussian3x3
   - CLGaussian5x5
   - CLGaussianPyramid
   - CLHOGDescriptor
   - CLHOGDetector
   - CLHOGGradient
   - CLHOGMultiDetection
   - CLHarrisCorners
   - CLHistogram
   - CLIntegralImage
   - CLLaplacianPyramid
   - CLLaplacianReconstruct
   - CLMagnitude
   - CLMeanStdDev
   - CLMedian3x3
   - CLMinMaxLocation
   - CLNonLinearFilter
   - CLOpticalFlow
   - CLPhase
   - CLScharr3x3
   - CLSobel3x3
   - CLSobel5x5
   - CLSobel7x7
   - CLTableLookup
   - CLThreshold
   - CLWarpAffine
   - CLWarpPerspective

v21.02 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Upgrade C++ standard to C++14
 - Add macOS support
 - Add Armv8-R AArch64 architecture support
 - Add SVE/SVE2 support for:
   - NEScaleKernel
   - @ref NEActivationLayer
   - @ref NEArithmeticAddition
   - NEBatchNormalizationLayerKernel
   - cpu::kernels::CpuLogits1DSoftmaxKernel
   - cpu::kernels::CpuLogits1DMaxKernel
   - cpu::kernels::CpuElementwiseUnaryKernel
 - Remove padding from OpenCL kernels:
   - CLDirectConvolutionLayerKernel
   - CLArgMinMaxLayerKernel
   - CLPadLayerKernel
   - CLROIAlignLayerKernel
   - CLRangeKernel
   - CLScaleKernel
   - CLSelectKernel
   - CLBitwiseKernel
   - opencl::kernels::ClFloorKernel
   - CLTransposeKernel
 - Deprecate functions in CLTuner:
    - add_lws_to_table
    - import_lws_table
    - lws_table
 - Remove functions:
   - NELocallyConnectedLayer / CLLocallyConnectedLayer
   - NEIm2Col
   - NECol2Im
   - NEGEMMInterleave4x4
   - NEGEMMTranspose1xW
   - NEComputeAllAnchors / CLComputeAllAnchors
   - NEGEMMAssemblyDispatch
   - NEUpsampleLayer / CLUpsampleLayer
 - Remove kernels:
   - NEGEMMMatrixVectorMultiplyKernel
   - NELocallyConnectedMatrixMultiplyKernel / CLLocallyConnectedMatrixMultiplyKernel
   - NEUpsampleLayerKernel / CLUpsampleLayerKernel
 - Extend OpenCL tuner with workgroup batch size support
   - Experimental extension for the OpenCL tuner to tune the batches of work groups distribute to compute units
 - Add functionality to load the OpenCL GEMM heuristics at runtime
   - The GEMM heuristic file (MLGO) can be used to update the default GEMM heuristics available for OpenCL
 - Note: there might be performance regressions against v20.08 in Inception v3 using int8 data types on Arm Mali-G77 GPUs. Currently under investigation
 - Note: data-type decoupling is in progress and experimental. Warning of unused symbols might be raised

v20.11 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Performance regressions can be noted when executing Depthwise Convolution on Arm® Neon™ with a depth multiplier > 1 for quantized data type.
   This is planned to be resolved in 21.02 release.
 - Added new data type QASYMM8_SIGNED support for @ref NEROIAlignLayer.
 - Added new data type S32 support for:
   - NEArithmeticSubtraction
   - NEArithmeticSubtractionKernel
   - @ref NEPixelWiseMultiplication
   - NEPixelWiseMultiplicationKernel
   - NEElementwiseDivision
   - NEDivisionOperationKernel
 - Interface change
   - Properly support softmax axis to have the same meaning as other major frameworks. That is, axis now defines the dimension
     on which Softmax/Logsoftmax is performed. E.g. for input of shape 4x5x6 and axis=1, softmax will be applied to 4x6=24 vectors of size 5.
     The supported value range of axis is [-rank, rank).
     This change applies to the following functions:
      - @ref NESoftmaxLayer
      - @ref NELogSoftmaxLayer
      - @ref CLSoftmaxLayer
      - @ref CLLogSoftmaxLayer
      - GCSoftmaxLayer
 - New OpenCL kernels / functions:
   - CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
   - @ref CLLogicalNot
   - @ref CLLogicalAnd
   - @ref CLLogicalOr
 - New Arm® Neon™ kernels / functions:
   - @ref NELogicalNot
   - @ref NELogicalAnd
   - @ref NELogicalOr
 - Removed padding from Arm® Neon™ kernels:
   - NEComplexPixelWiseMultiplicationKernel
   - NENonMaximaSuppression3x3Kernel
   - NERemapKernel
   - NEGEMMInterleave4x4Kernel
   - NEDirectConvolutionLayerKernel
   - NEScaleKernel
   - NELocallyConnectedMatrixMultiplyKernel
   - NEGEMMLowpOffsetContributionKernel
   - NEGEMMTranspose1xWKernel
   - NEPoolingLayerKernel
   - NEConvolutionKernel
   - NEDepthwiseConvolutionLayerNativeKernel
   - NEGEMMLowpMatrixMultiplyKernel
   - NEGEMMMatrixMultiplyKernel
   - NEDirectConvolutionLayerOutputStageKernel
   - NEReductionOperationKernel
   - NEGEMMLowpMatrixAReductionKernel
   - NEGEMMLowpMatrixBReductionKernel
 - Removed padding from OpenCL kernels:
   - CLBatchConcatenateLayerKernel
   - CLElementwiseOperationKernel
   - CLBatchNormalizationLayerKernel
   - CLPoolingLayerKernel
   - CLWinogradInputTransformKernel
   - CLGEMMLowpMatrixMultiplyNativeKernel
   - CLGEMMLowpMatrixAReductionKernel
   - CLGEMMLowpMatrixBReductionKernel
   - CLGEMMLowpOffsetContributionOutputStageKernel
   - CLGEMMLowpOffsetContributionKernel
   - CLWinogradOutputTransformKernel
   - CLGEMMLowpMatrixMultiplyReshapedKernel
   - CLFuseBatchNormalizationKernel
   - CLDepthwiseConvolutionLayerNativeKernel
   - CLDepthConvertLayerKernel
   - CLCopyKernel
   - CLDepthwiseConvolutionLayer3x3NHWCKernel
   - CLActivationLayerKernel
   - CLWinogradFilterTransformKernel
   - CLWidthConcatenateLayerKernel
   - CLWidthConcatenate4TensorsKernel
   - CLWidthConcatenate2TensorsKernel
   - CLLogits1DMaxShiftExpSumKernel
   - CLLogits1DNormKernel
   - CLHeightConcatenateLayerKernel
   - CLGEMMMatrixMultiplyKernel
   - CLGEMMLowpQuantizeDownInt32ScaleKernel
   - CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
   - CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
   - CLDepthConcatenateLayerKernel
   - CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
 - Removed OpenCL kernels / functions:
   - CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
   - CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
   - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
 - Deprecated OpenCL kernels / functions (If a kernel is used only by the function that is being deprecated, the kernel is deprecated together):
     - CLLocallyConnectedLayer
     - CLLocallyConnectedMatrixMultiplyKernel
     - CLAbsoluteDifference
     - CLAbsoluteDifferenceKernel
     - CLAccumulate
     - CLAccumulateKernel
     - CLAccumulateSquared
     - CLAccumulateSquaredKernel
     - CLAccumulateWeighted
     - CLAccumulateWeightedKernel
     - CLAccumulateWeightedFP16Kernel
     - CLBox3x3
     - CLBox3x3Kernel
     - CLBox3x3FP16Kernel
     - CLCannyEdge
     - CLChannelCombine
     - CLChannelCombineKernel
     - CLChannelExtract
     - CLChannelExtractKernel
     - CLColorConvert
     - CLColorConvertKernel
     - CLConvolution3x3
     - CLConvolutionRectangle
     - CLConvolutionRectangleKernel
     - CLConvolutionSquare
     - CLConvolutionKernel
     - CLDerivative
     - CLDerivativeKernel
     - CLDilate
     - CLDilateKernel
     - CLEqualizeHistogram
     - CLErode
     - CLErodeKernel
     - CLFastCorners
     - CLFastCornersKernel
     - CLGaussian3x3
     - CLGaussian3x3Kernel
     - CLGaussian5x5
     - CLGaussian5x5HorKernel
     - CLGaussian5x5VertKernel
     - CLGaussianPyramid
     - CLGaussianPyramidHalf
     - CLGaussianPyramidOrb
     - CLHarrisCorners
     - CLHarrisScoreKernel
     - CLHarrisScoreFP16Kernel
     - CLHistogram
     - CLHistogramKernel
     - CLHOGOrientationBinningKernel
     - CLHOGBlockNormalizationKernel
     - CLHOGDetectorKernel
     - CLHOGNonMaximaSuppressionKernel
     - CLHOGDescriptor
     - CLHOGDetector
     - CLHOGGradient
     - CLHOGMultiDetection
     - CLHOGOrientationBinningKernel
     - CLHOGBlockNormalizationKernel
     - CLHOGDetectorKernel
     - CLIntegralImage
     - CLIntegralImageKernel
     - CLLaplacianReconstruct
     - CLLaplacianPyramid
     - CLMagnitude
     - CLMagnitudePhaseKernel
     - CLMedian3x3
     - CLMedian3x3Kernel
     - CLMinMaxLocation
     - CLMinMaxLocationKernel
     - CLNonLinearFilter
     - CLNonLinearFilterKernel
     - CLNonMaximaSuppression3x3
     - CLNonMaximaSuppression3x3FP16Kernel
     - CLNonMaximaSuppression3x3Kernel
     - CLOpticalFlow
     - CLPhase
     - CLRemap
     - CLRemapKernel
     - CLScharr3x3
     - CLScharr3x3Kernel
     - CLSobel3x3
     - CLSobel3x3Kernel
     - CLSobel5x5
     - CLSobel5x5HorKernel
     - CLSobel5x5VertKernel
     - CLSobel7x7
     - CLSobel7x7HorKernel
     - CLSobel7x7VertKernel
     - CLThreshold
     - CLThresholdKernel
     - CLWarpAffine
     - CLWarpAffineKernel
     - CLWarpPerspective
     - CLWarpPerspectiveKernel
 - Deprecated Arm® Neon™ kernels / functions (If a kernel is used only by the function that is being deprecated, the kernel is deprecated together):
     - NELocallyConnectedLayer
     - NELocallyConnectedMatrixMultiplyKernel
     - NEAbsoluteDifference
     - NEAbsoluteDifferenceKernel
     - NEAccumulate
     - NEAccumulateKernel
     - NEAccumulateSquared
     - NEAccumulateSquaredKernel
     - NEAccumulateWeighted
     - NEAccumulateWeightedKernel
     - NEAccumulateWeightedFP16Kernel
     - NEBox3x3
     - NEBox3x3Kernel
     - NEBox3x3FP16Kernel
     - NECannyEdge
     - NEChannelCombine
     - NEChannelCombineKernel
     - NEChannelExtract
     - NEChannelExtractKernel
     - NEColorConvert
     - NEColorConvertKernel
     - NEConvolution3x3
     - NEConvolutionRectangle
     - NEConvolutionRectangleKernel
     - NEConvolutionSquare
     - NEConvolutionKernel
     - NEDerivative
     - NEDerivativeKernel
     - NEDilate
     - NEDilateKernel
     - NEEqualizeHistogram
     - NEErode
     - NEErodeKernel
     - NEFastCorners
     - NEFastCornersKernel
     - NEGaussian3x3
     - NEGaussian3x3Kernel
     - NEGaussian5x5
     - NEGaussian5x5HorKernel
     - NEGaussian5x5VertKernel
     - NEGaussianPyramid
     - NEGaussianPyramidHalf
     - NEGaussianPyramidOrb
     - NEHarrisCorners
     - NEHarrisScoreKernel
     - NEHarrisScoreFP16Kernel
     - NEHistogram
     - NEHistogramKernel
     - NEHOGOrientationBinningKernel
     - NEHOGBlockNormalizationKernel
     - NEHOGDetectorKernel
     - NEHOGNonMaximaSuppressionKernel
     - NEHOGDescriptor
     - NEHOGDetector
     - NEHOGGradient
     - NEHOGMultiDetection
     - NEHOGOrientationBinningKernel
     - NEHOGBlockNormalizationKernel
     - NEHOGDetectorKernel
     - NEIntegralImage
     - NEIntegralImageKernel
     - NELaplacianReconstruct
     - NELaplacianPyramid
     - NEMagnitude
     - NEMagnitudePhaseKernel
     - NEMedian3x3
     - NEMedian3x3Kernel
     - NEMinMaxLocation
     - NEMinMaxLocationKernel
     - NENonLinearFilter
     - NENonLinearFilterKernel
     - NENonMaximaSuppression3x3
     - NENonMaximaSuppression3x3FP16Kernel
     - NENonMaximaSuppression3x3Kernel
     - NEOpticalFlow
     - NEPhase
     - NERemap
     - NERemapKernel
     - NEScharr3x3
     - NEScharr3x3Kernel
     - NESobel3x3
     - NESobel3x3Kernel
     - NESobel5x5
     - NESobel5x5HorKernel
     - NESobel5x5VertKernel
     - NESobel7x7
     - NESobel7x7HorKernel
     - NESobel7x7VertKernel
     - NEThreshold
     - NEThresholdKernel
     - NEWarpAffine
     - NEWarpAffineKernel
     - NEWarpPerspective
     - NEWarpPerspectiveKernel
 - Deprecated GLES kernels / functions (If a kernel is used only by the function that is being deprecated, the kernel is deprecated together):
     - GCAbsoluteDifference
     - GCActivationLayer
     - GCArithmeticAddition
     - GCBatchNormalizationLayer
     - GCConcatenateLayer
     - GCConvolutionLayer
     - GCDepthwiseConvolutionLayer
     - GCDirectConvolutionLayer
     - GCDropoutLayer
     - GCFillBorder
     - GCFullyConnectedLayer
     - GCGEMM
     - GCGEMMInterleave4x4
     - GCGEMMTranspose1xW
     - GCNormalizationLayer
     - GCNormalizePlanarYUVLayer
     - GCPixelWiseMultiplication
     - GCPoolingLayer
     - GCScale
     - GCSoftmaxLayer
     - GCTensorShift
     - GCTranspose


v20.08 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Added new data type QASYMM8_SIGNED support for:
   - @ref CLArgMinMaxLayer
   - CLArgMinMaxLayerKernel
 - Added new data type U8 support for:
   - NECropKernel
   - CLCropKernel
 - Added align_corner support for nearest neighbor interpolation in:
   - NEScaleKernel
   - CLScaleKernel
 - New OpenCL kernels / functions:
   - CLMaxUnpoolingLayerKernel
 - New Arm® Neon™ kernels / functions:
   - NEMaxUnpoolingLayerKernel
 - New graph example:
   - graph_yolov3_output_detector
 - GEMMTuner improvements:
   - Added fp16 support
   - Output json files for easier integration
   - Enabled tuning for export_to_cl_image_rhs option for RHS tensors
   - More robust script for running benchmarks
 - Removed padding from:
   - NEPixelWiseMultiplicationKernel
   - NEHeightConcatenateLayerKernel
   - NEThresholdKernel
   - NEBatchConcatenateLayerKernel
   - NETransposeKernel
   - NEBatchNormalizationLayerKernel
   - NEArithmeticSubtractionKernel
   - NEBoundingBoxTransformKernel
   - NELogits1DMaxKernel
   - NELogits1DSoftmaxKernel
   - NEROIPoolingLayerKernel
   - NEROIAlignLayerKernel
   - NEYOLOLayerKernel
   - NEUpsampleLayerKernel
   - NEFloorKernel
   - NEWidthConcatenateLayerKernel
   - NEDepthConcatenateLayerKernel
   - NENormalizationLayerKernel
   - NEL2NormalizeLayerKernel
   - NEFillArrayKernel
   - NEDepthConvertLayerKernel
   - NERangeKernel
   - @ref NEPriorBoxLayer
 - Removed OpenCL kernels / functions:
   - CLGEMMLowpQuantizeDownInt32ToUint8Scale
   - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat
 - Removed Arm® Neon™ kernels / functions:
   - NEGEMMLowpQuantizeDownInt32ToUint8Scale
   - NEGEMMMatrixAccumulateBiasesKernel
 - Deprecated functions / interfaces:
   - Non-descriptor based interfaces for NEThreshold, CLThreshold
   - Non-descriptor based interfaces for @ref NEScale, @ref CLScale and GCScale
   - In @ref NESoftmaxLayer, @ref NELogSoftmaxLayer, @ref CLSoftmaxLayer, @ref CLLogSoftmaxLayer and GCSoftmaxLayer :
      The default "axis" value for @ref CLSoftmaxLayer, @ref CLLogSoftmaxLayer and GCSoftmaxLayer is changed from 1 to 0.
      Only axis 0 is supported.
      The default "axis" value for @ref NESoftmaxLayer, @ref NELogSoftmaxLayer is changed from 1 to 0.
      Only axis 0 is supported.
 - The support for quantized data types has been removed from @ref CLLogSoftmaxLayer due to implementation complexity.
 - Removed padding requirement for the input (e.g. LHS of GEMM) and output in CLGEMMMatrixMultiplyNativeKernel, CLGEMMMatrixMultiplyReshapedKernel, CLGEMMMatrixMultiplyReshapedOnlyRHSKernel and CLIm2ColKernel (NHWC only)
   - This change allows to use @ref CLGEMMConvolutionLayer without extra padding for the input and output.
   - Only the weights/bias of @ref CLGEMMConvolutionLayer could require padding for the computation.
   - Only on Arm® Mali™ Midgard GPUs, @ref CLGEMMConvolutionLayer could require padding since CLGEMMMatrixMultiplyKernel is called and currently requires padding.
 - Added support for exporting the OpenCL buffer object to the OpenCL image object in CLGEMMMatrixMultiplyReshapedKernel and CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.
   - This support allows to export the OpenCL buffer used for the reshaped RHS matrix to the OpenCL image object.
   - The padding requirement for the OpenCL image object is considered into the CLGEMMReshapeRHSMatrixKernel.
   - The reshaped RHS matrix stores the weights when GEMM is used to accelerate CLGEMMConvolutionLayer.

v20.05 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Updated recommended NDK version to r18b.
 - Updated recommended gcc version to Linaro 6.3.1.
 - Added Bfloat16 type support
 - Added Bfloat16 support in:
     - NEWeightsReshapeKernel
     - NEConvolutionLayerReshapeWeights
     - NEIm2ColKernel
     - NEIm2Col
     - NEDepthConvertLayerKernel
     - @ref NEDepthConvertLayer
     - @ref NEGEMMConvolutionLayer
     - NEGEMMAssemblyDispatch
 - Added new data type QASYMM8_SIGNED support for:
     - @ref CLDirectConvolutionLayer
     - @ref CLDeconvolutionLayer
     - @ref CLDirectDeconvolutionLayer
     - @ref CLGEMMDeconvolutionLayer
     - CLGEMMLowpMatrixMultiplyReshapedKernel
     - CLGEMMLowpQuantizeDownInt32ScaleKernel
     - CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
     - @ref CLReductionOperation
     - @ref CLReduceMean
     - @ref NEScale
     - NEScaleKernel
     - NEUpsampleLayer
     - @ref NECast
     - @ref NEReductionOperation
     - @ref NEReduceMean
     - @ref NEArgMinMaxLayer
     - @ref NEDeconvolutionLayer
     - NEGEMMLowpQuantizeDownInt32ScaleKernel
     - @ref CPPBoxWithNonMaximaSuppressionLimit
     - @ref CPPDetectionPostProcessLayer
     - @ref CPPPermuteKernel
     - @ref CPPPermute
     - @ref CPPTopKVKernel
     - @ref CPPTopKV
     - @ref CPPUpsample
     - @ref CPPUpsampleKernel
 - New OpenCL kernels / functions:
     - @ref CLQLSTMLayer
     - CLQLSTMLayerNormalizationKernel
 - New Arm® Neon™ kernels / functions:
     - @ref NEQLSTMLayer
     - NEQLSTMLayerNormalizationKernel
 - Added HARD_SWISH support in:
     - CLActivationLayerKernel
     - NEActivationLayerKernel
 - Deprecated OpenCL kernels / functions:
     - CLGEMMLowpQuantizeDownInt32ToUint8Scale
     - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat
 - Deprecated Arm® Neon™ kernels / functions:
     - NEGEMMLowpQuantizeDownInt32ToUint8Scale
 - Removed CPP kernels / functions:
     - CPPFlipWeightsKernel
 - Removed PoolingLayerInfo constructors without Data Layout.
 - Removed CLDepthwiseConvolutionLayer3x3
 - Removed NEDepthwiseConvolutionLayerOptimized
 - Added support for Winograd 3x3,4x4 on Arm® Neon™ FP16:
     - @ref NEWinogradConvolutionLayer
     - CpuWinogradConv2dTransformInputKernel
     - CpuWinogradConv2dTransformOutputKernel
     - CpuWinogradConv2dTransformWeightsKernel
 - Added CLCompileContext
 - Added Arm® Neon™ GEMM kernel with 2D window support

v20.02.1 Maintenance release
 - Added Android-NN build script.

v20.02 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Added new data type QASYMM8_SIGNED support for:
     - @ref CLDepthwiseConvolutionLayer
     - CLDepthwiseConvolutionLayer3x3
     - @ref CLGEMMConvolutionLayer
     - CLGEMMLowpMatrixMultiplyCore
     - CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
     - CLGEMMLowpMatrixMultiplyNativeKernel
     - @ref NEActivationLayer
     - NEComparisonOperationKernel
     - @ref NEConvolutionLayer
     - @ref NEDepthwiseConvolutionLayer
     - NEDepthwiseConvolutionLayer3x3Kernel
     - NEDirectConvolutionLayerOutputStageKernel
     - @ref NEElementwiseComparison
     - @ref NEElementwiseMax
     - @ref NEElementwiseMin
     - @ref NEElementwiseSquaredDiff
     - @ref NEFullyConnectedLayer
     - NEGEMMMatrixVectorMultiplyKernel
     - @ref NEPixelWiseMultiplication
     - @ref NEPoolingLayer
     - @ref NEPReluLayer
 - Added support for QSYMM8_PER_CHANNEL in:
     - NEDepthwiseConvolutionLayer3x3Kernel
 - Added support for split sizes in:
     - @ref CLSplit
     - @ref NESplit
 - New OpenCL kernels / functions:
     - @ref CLFill
     - CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel / CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint
 - New Arm® Neon™ kernels / functions:
     - @ref NEFill
     - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel / NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint
 - Deprecated Arm® Neon™ functions / interfaces:
     - CLDepthwiseConvolutionLayer3x3
     - NEDepthwiseConvolutionLayerOptimized
     - PoolingLayerInfo constructors without Data Layout.
 - Added support for quantization with multiplier greater than 1 on Arm® Neon™ and CL.
 - Added support for quantized inputs of type QASYMM8_SIGNED and QASYMM8 to @ref CLQuantizationLayer.
 - Added the ability to build bootcode for bare metal.
 - Added support for generating synthetic QASYMM8 graphs.
 - Added support for F16 datatype in VGG16.
 - Removed pre-built binaries for GLES.

v19.11.1 Public maintenance release
 - Fix offset calculation in NEReductionOperationKernel.
 - Fix data layout in NEScaleKernel for nhwc.
 - Retain configuration step data layout to avoid side-effects.
 - Perform sqrt in double domain for L2 pooling.
 - Fix output shape calculation for Reduce Mean
 - Restrict cases where optimized NEPadLayer runs.

v19.11 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Updated recommended NDK version to r17c.
 - Deprecated OpenCL kernels / functions:
    - CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel
    - CLDepthwiseIm2ColKernel
    - CLDepthwiseSeparableConvolutionLayer
    - CLDepthwiseVectorToTensorKernel
    - CLDirectConvolutionLayerOutputStageKernel
 - Deprecated Arm® Neon™ kernels / functions:
    - NEDepthwiseWeightsReshapeKernel
    - NEDepthwiseIm2ColKernel
    - NEDepthwiseSeparableConvolutionLayer
    - NEDepthwiseVectorToTensorKernel
    - NEDepthwiseConvolutionLayer3x3
 - New OpenCL kernels / functions:
    - CLInstanceNormalizationLayerKernel / @ref CLInstanceNormalizationLayer
    - CLDepthwiseConvolutionLayerNativeKernel to replace the old generic depthwise convolution (see Deprecated
      OpenCL kernels / functions)
    - @ref CLLogSoftmaxLayer
 - New Arm® Neon™ kernels / functions:
    - NEBoundingBoxTransformKernel / @ref NEBoundingBoxTransform
    - NEComputeAllAnchorsKernel / NEComputeAllAnchors
    - @ref NEDetectionPostProcessLayer
    - @ref NEGenerateProposalsLayer
    - NEInstanceNormalizationLayerKernel / @ref NEInstanceNormalizationLayer
    - @ref NELogSoftmaxLayer
    - NEROIAlignLayerKernel / @ref NEROIAlignLayer
 - Added QASYMM8 support for:
    - @ref CLGenerateProposalsLayer
    - @ref CLROIAlignLayer
    - @ref CPPBoxWithNonMaximaSuppressionLimit
 - Added QASYMM16 support for:
    - @ref CLBoundingBoxTransform
 - Added FP16 support for:
    - CLGEMMMatrixMultiplyReshapedKernel
 - Added new data type QASYMM8_PER_CHANNEL support for:
    - CLDequantizationLayer
    - @ref NEDequantizationLayer
 - Added new data type QSYMM8_PER_CHANNEL support for:
    - @ref CLConvolutionLayer
    - @ref NEConvolutionLayer
    - @ref CLDepthwiseConvolutionLayer
    - @ref NEDepthwiseConvolutionLayer
 - Added FP16 mixed-precision support for:
    - CLGEMMMatrixMultiplyReshapedKernel
    - CLPoolingLayerKernel
 - Added FP32 and FP16 ELU activation for:
    - @ref CLActivationLayer
    - @ref NEActivationLayer
 - Added asymmetric padding support for:
    - @ref CLDirectDeconvolutionLayer
    - @ref CLGEMMDeconvolutionLayer
    - @ref NEDeconvolutionLayer
 - Added SYMMETRIC and REFLECT modes for CLPadLayerKernel / @ref CLPadLayer.
 - Replaced the calls to NECopyKernel and NEMemsetKernel with @ref NEPadLayer in @ref NEGenerateProposalsLayer.
 - Replaced the calls to CLCopyKernel and CLMemsetKernel with @ref CLPadLayer in @ref CLGenerateProposalsLayer.
 - Improved performance for CL Inception V3 - FP16.
 - Improved accuracy for CL Inception V3 - FP16 by enabling FP32 accumulator (mixed-precision).
 - Improved Arm® Neon™ performance by enabling fusing batch normalization with convolution and depth-wise convolution layer.
 - Improved Arm® Neon™ performance for MobileNet-SSD by improving the output detection performance.
 - Optimized @ref CLPadLayer.
 - Optimized CL generic depthwise convolution layer by introducing CLDepthwiseConvolutionLayerNativeKernel.
 - Reduced memory consumption by implementing weights sharing.

v19.08.1 Public maintenance release
 - Fix offset calculation in NEReductionOperationKernel.
 - Fix data layout in NEScaleKernel for nhwc.
 - Retain configuration step data layout to avoid side-effects.
 - Perform sqrt in double domain for L2 pooling.
 - Fix output shape calculation for Reduce Mean
 - Fix broadcast CLPixelwiseMultiplication with 5D tensors

v19.08 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Deprecated Arm® Neon™ functions
    - NEDepthConcatenateLayer
    - NEWidthConcatenateLayer
 - Deprecated OpenCL kernels / functions
    - CLDepthConcatenateLayer
    - CLGEMMInterleave4x4Kernel / CLGEMMInterleave4x4
    - CLGEMMTranspose1xWKernel / CLGEMMTranspose1xW
    - CLWidthConcatenateLayer
 - New Arm® Neon™ kernels / functions:
    - @ref NEAbsLayer
    - @ref NECast
    - @ref NEElementwisePower
    - @ref NELogLayer
    - @ref NELSTMLayerQuantized
    - @ref NENegLayer
    - @ref NEPReluLayer
    - @ref NESinLayer
    - NEBatchConcatenateLayerKernel
    - NEDepthToSpaceLayerKernel / @ref NEDepthToSpaceLayer
    - NEDepthwiseConvolutionLayerNativeKernel
    - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
    - NEMeanStdDevNormalizationKernel / @ref NEMeanStdDevNormalizationLayer
    - NESpaceToDepthLayerKernel / @ref NESpaceToDepthLayer
 - New OpenCL kernels / functions:
    - @ref CLAbsLayer
    - @ref CLElementwisePower
    - @ref CLLogLayer
    - @ref CLLSTMLayerQuantized
    - @ref CLNegLayer
    - @ref CLPReluLayer
    - @ref CLSinLayer
    - CLBatchConcatenateLayerKernel
    - CLDepthToSpaceLayerKernel / @ref CLDepthToSpaceLayer
    - CLGEMMLowpMatrixMultiplyNativeKernel
    - CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
    - CLGEMMMatrixMultiplyNativeKernel
    - CLMeanStdDevNormalizationKernel /CLMeanStdDevNormalizationLayer
    - CLSpaceToDepthLayerKernel / @ref CLSpaceToDepthLayer
 - New examples:
    - neon_opticalflow
    - cl_cache
    - neon_permute
 - Added support for FP16 in @ref NEDeconvolutionLayer
 - Added support for FP16 in @ref CLDeconvolutionLayer
 - Added support for REDUCE_MIN and REDUCE_MAX in @ref ReductionOperation
 - Enable the fusion of batch normalization with convolution and depthwise convolution layer for FP32 in the graph API (OpenCL only)
 - Added support for fusing activation function and broadcast addition with the matrix multiplication for FP32 (OpenCL only)
 - Re-factored the depthwise convolution layer kernel on Arm® Neon™ for generic cases
 - Added an optimized depthwise convolution layer kernel for 5x5 filters (Neon™ only)
 - Added support to enable OpenCL kernel cache. Added example showing how to load the prebuilt OpenCL kernels from a binary cache file
 - Altered @ref QuantizationInfo interface to support per-channel quantization.
 - The CLDepthwiseConvolutionLayer3x3 will be included by @ref CLDepthwiseConvolutionLayer to accommodate for future optimizations.
 - The NEDepthwiseConvolutionLayerOptimized will be included by @ref NEDepthwiseConvolutionLayer to accommodate for future optimizations.
 - Removed inner_border_right and inner_border_top parameters from @ref CLDeconvolutionLayer interface
 - Removed inner_border_right and inner_border_top parameters from @ref NEDeconvolutionLayer interface
 - Optimized the Arm® Neon™ assembly kernel for GEMMLowp. The new implementation fuses the output stage and quantization with the matrix multiplication kernel

v19.05 Public major release
 - Various bug fixes.
 - Various optimisations.
 - New Arm® Neon™ kernels / functions:
    - NEBatchToSpaceLayerKernel / @ref NEBatchToSpaceLayer
    - NEComplexPixelWiseMultiplicationKernel / @ref NEComplexPixelWiseMultiplication
    - NECropKernel / @ref NECropResize
    - NEDepthwiseConvolutionAssemblyDispatch
    - NEFFTDigitReverseKernel
    - NEFFTRadixStageKernel
    - NEFFTScaleKernel
    - NEGEMMLowpOffsetContributionOutputStageKernel
    - NEHeightConcatenateLayerKernel
    - NESpaceToBatchLayerKernel / @ref NESpaceToBatchLayer
    - @ref NEFFT1D
    - @ref NEFFT2D
    - @ref NEFFTConvolutionLayer
 - New OpenCL kernels / functions:
    - CLComplexPixelWiseMultiplicationKernel / @ref CLComplexPixelWiseMultiplication
    - CLCropKernel / @ref CLCropResize
    - CLDeconvolutionReshapeOutputKernel
    - CLFFTDigitReverseKernel
    - CLFFTRadixStageKernel
    - CLFFTScaleKernel
    - CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
    - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
    - CLHeightConcatenateLayerKernel
    - @ref CLDirectDeconvolutionLayer
    - @ref CLFFT1D
    - @ref CLFFT2D
    - @ref CLFFTConvolutionLayer
    - @ref CLGEMMDeconvolutionLayer
 - New OpenGLES kernels / functions:
    - GCConcatenateLayer
 - Deprecated functions/interfaces
    - GCDepthConcatenateLayer
    - NEWidthConcatenateLayer
    - NEDepthConcatenateLayer
    - CLWidthConcatenateLayer
    - CLDepthConcatenateLayer
    - CLGEMMInterleave4x4
    - CLGEMMTranspose1xW
 - Support different quantization info in CLConcatLayer.
 - Add checks on different input/output quantization info were not supported.
 - Tensors have different quantization information.
 - Add FP16 support checks.
 - Fix output quantization CLDeptwiseConv3x3 when activation is fused.
 - New graph examples:
     - graph_convolution
     - graph_fully_connected
     - graph_depthwise_convolution
     - Deepspeech v0.4.1
 - Add support for QASYMM8 in NEArithmeticSubtractionKernel.
 - Add support for QASYMM8 in NEPixelWiseMultiplicationKernel.
 - Add support for QASYMM8 NEDeconvolution.
 - Add support for DequantizationLayer for Neon/CL.
 - Add support for dilation in CLDepthwiseConvolution.
 - Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore.
 - Optimize CLDeconvolution.
 - Add StackLayer to the graph API.
 - Add support for "reflect" padding mode in NEPad.
 - Winograd 7x7 NHWC on OpenCL.
 - Rework CL ML layers to run exclusively on CL.
 - Support different quantization info in PoolingLayer.
 - Implement and test import memory interfaces.
 - Added new tests and removed old ones.
 - Various clang-tidy fixes.

v19.02 Public major release
 - Various bug fixes.
 - Various optimisations.
 - New Arm® Neon™ kernels / functions:
    - NETileKernel / @ref NETile
    - NEFuseBatchNormalizationKernel / @ref NEFuseBatchNormalization
    - NEElementwiseOperationKernel
    - @ref NEElementwiseMax
    - @ref NEElementwiseMin
    - @ref NEElementwiseSquaredDiff
    - NESelectKernel / @ref NESelect
    - @ref NESplit
    - @ref NESlice
    - @ref NEUnstack
    - NEStridedSliceKernel / @ref NEStridedSlice
    - NEElementwiseUnaryKernel
    - @ref NERsqrtLayer
    - @ref NEExpLayer
    - NEReverseKernel / @ref NEReverse
    - @ref NEArgMinMaxLayer
    - NEStackLayerKernel / @ref NEStackLayer
    - NERangeKernel / @ref NERange
    - @ref NEPadLayer
    - NEMemsetKernel
    - NEGatherKernel / @ref NEGather
    - @ref NEElementwiseComparison
    - @ref NEElementwiseComparisonStatic
    - NEComparisonOperationKernel
    - @ref NEElementwiseDivision
 - New OpenCL kernels / functions:
    - CLSelectKernel / @ref CLSelect
    - CLTileKernel / @ref CLTile
    - CLComparisonKernel / @ref CLComparison
    - @ref CLArgMinMaxLayer
    - @ref CLElementwiseMax
    - @ref CLElementwiseMin
    - @ref CLElementwiseSquaredDiff
    - CLStackLayerKernel / @ref CLStackLayer
    - @ref CLReverse / CLReverseKernel
    - @ref CLRsqrtLayer
    - @ref CLExpLayer
    - CLElementWiseUnaryLayerKernel
    - CLGEMMReshapeLHSMatrixKernel
    - CLGEMMReshapeRHSMatrixKernel
    - CLGEMMMatrixMultiplyReshapedKernel
    - CLRangeKernel / @ref CLRange
    - @ref CLUnstack
    - CLGatherKernel / @ref CLGather
    - CLGEMMLowpMatrixMultiplyReshapedKernel
 - New CPP kernels / functions:
    - @ref CPPDetectionOutputLayer
    - @ref CPPTopKV / @ref CPPTopKVKernel
 - Added new examples:
    - graph_ssd_mobilenet.cpp
    - graph_mobilenet_v2.cpp
    - graph_resnet12.cpp
    - graph_srcnn955.cpp
    - graph_vgg_vdsr.cpp
    - graph_inception_resnet_v1.cpp
 - Add 4D tensors support to
    - @ref NESoftmaxLayer
 - Fused activation in @ref CLWinogradConvolutionLayer
 - Extended @ref NEPermute to support more cases
 - Added Neon™/SVE GEMM Hybrid kernels
 - Added u8 and s8 hybrid assembly kernels
 - Introduced GEMM strategy name in NEGEMMAssemblyWrapper
 - Improved @ref CLTuner
 - Fused the bias addition within @ref CLGEMM
 - Added support for QASYMM8 LOGISTIC activation in @ref NEActivationLayer
 - Added NHWC data layout support to:
    - @ref NEScale for F16
    - @ref CLNormalizationLayer IN_MAP_2D for FP32/FP16
    - @ref NEL2NormalizeLayer for FP32/FP16
    - @ref NENormalizationLayer IN_MAP_2D for FP32/FP16
    - @ref CLROIAlignLayer
    - @ref CLGenerateProposalsLayer
 - Added QASYMM8 support to the following kernels:
    - NEArithmeticAdditionKernel
    - @ref NEScale
 - Added new tests and improved validation and benchmarking suites.
 - Deprecated functions/interfaces
    - Usage of inner_border_right and inner_border_top has been deprecated in @ref CLDeconvolutionLayer and @ref NEDeconvolutionLayer

v18.11 Public major release
 - Various bug fixes.
 - Various optimisations.
 - New Arm® Neon™ kernels / functions:
    - @ref NEChannelShuffleLayer / NEChannelShuffleLayerKernel
    - @ref NEReduceMean
    - @ref NEReorgLayer / NEReorgLayerKernel
    - @ref NEPriorBoxLayer / NEPriorBoxLayerKernel
    - NEUpsampleLayer / NEUpsampleLayerKernel
    - NEYOLOLayer / NEYOLOLayerKernel
 - New OpenCL kernels / functions:
    - @ref CLBatchToSpaceLayer / CLBatchToSpaceLayerKernel
    - @ref CLBoundingBoxTransform / CLBoundingBoxTransformKernel
    - CLComputeAllAnchorsKernel
    - @ref CLGenerateProposalsLayer
    - @ref CLNormalizePlanarYUVLayer / CLNormalizePlanarYUVLayerKernel
    - @ref CLReorgLayer / CLReorgLayerKernel
    - @ref CLSpaceToBatchLayer / CLSpaceToBatchLayerKernel
    - @ref CLPadLayer
    - @ref CLReduceMean
    - @ref CLPriorBoxLayer / CLPriorBoxLayerKernel
    - @ref CLROIAlignLayer / CLROIAlignLayerKernel
    - @ref CLSlice
    - @ref CLSplit
    - @ref CLStridedSlice / CLStridedSliceKernel
    - CLUpsampleLayer / CLUpsampleLayerKernel
    - CLYOLOLayer / CLYOLOLayerKernel
 - New CPP kernels / functions:
    - @ref CPPBoxWithNonMaximaSuppressionLimit / @ref CPPBoxWithNonMaximaSuppressionLimitKernel
 - Added the validate method in:
    - @ref NEDepthConvertLayer
    - @ref NEFloor / @ref CLFloor
    - NEGEMMMatrixAdditionKernel
    - @ref NEReshapeLayer / @ref CLReshapeLayer
    - @ref CLScale
 - Added new examples:
    - graph_shufflenet.cpp
    - graph_yolov3.cpp
 - Added documentation for add a new function or kernel.
 - Improved doxygen documentation adding a list of the existing functions.
 - Add 4D tensors support to
    - CLWidthConcatenateLayer
    - CLFlattenLayer
    - @ref CLSoftmaxLayer
 - Add dot product support for CLDepthwiseConvolutionLayer3x3NHWCKernel non-unit stride
 - Add SVE support
 - Fused batch normalization into convolution layer weights in @ref CLFuseBatchNormalization
 - Fuses activation in CLDepthwiseConvolutionLayer3x3NCHWKernel, CLDepthwiseConvolutionLayer3x3NHWCKernel and @ref NEGEMMConvolutionLayer
 - Added NHWC data layout support to:
    - @ref CLChannelShuffleLayer
    - @ref CLDeconvolutionLayer
    - @ref CLL2NormalizeLayer
 - Added QASYMM8 support to the following kernels:
    - CLScaleKernel
    - NEDepthwiseConvolutionLayer3x3Kernel
    - CLPixelWiseMultiplicationKernel
 - Added FP16 support to the following kernels:
    - CLDepthwiseConvolutionLayer3x3NHWCKernel
    - NEDepthwiseConvolutionLayer3x3Kernel
    - CLNormalizePlanarYUVLayerKernel
    - @ref CLWinogradConvolutionLayer (5x5 kernel)
 - More tests added to both validation and benchmarking suites.

v18.08 Public major release
 - Various bug fixes.
 - Various optimisations.
 - Updated recommended NDK version to r17b.
 - Removed support for QS8/QS16 data types.
 - Added support for grouped convolution in @ref CLConvolutionLayer.
 - Added NHWC data layout support to:
    - NEDepthConcatenateLayer / CLDepthConcatenateLayer
    - @ref NEWinogradConvolutionLayer / @ref CLWinogradConvolutionLayer
    - @ref CLDepthwiseConvolutionLayer
    - @ref CLDirectConvolutionLayer
    - @ref CLConvolutionLayer
    - @ref CLScale
    - CLIm2ColKernel
 - New Arm® Neon™ kernels / functions:
    - @ref NERNNLayer
 - New OpenCL kernels / functions:
    - @ref CLArithmeticDivision
 - Introduced prepare() stage support in the graph API for GLES.
 - Added support for memory reusage when trying to allocate smaller CLTensors.
 - Enabled NHWC execution on graph examples.
 - Added JPEG accessor for validation purposes.
 - Added validate methods to some kernels / functions.

v18.05 Public major release
 - Various bug fixes.
 - Various optimisations.
 - 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.
 - Moved Neon™ assembly kernels to the folder src/core/Neon/kernels/arm_gemm.
 - Improved doxygen documentation.
 - Improved memory management for layer's transitions.
 - Added support for NHWC data layout in tensors.
 - Added NHWC data layout support to:
    - @ref NEGEMMConvolutionLayer
    - @ref NEDirectConvolutionLayer
    - @ref NEPoolingLayer / @ref CLPoolingLayer
    - @ref NEBatchNormalizationLayer / @ref CLBatchNormalizationLayer
    - @ref NEDepthwiseConvolutionLayer
    - @ref NEScale
    - NEIm2Col
 - Added support for dilated convolutions in @ref NEConvolutionLayer and @ref CLConvolutionLayer.
 - New OpenCL kernels / functions:
    - @ref CLChannelShuffleLayer / CLChannelShuffleLayerKernel
    - CLConvertFullyConnectedWeightsKernel / @ref CLConvertFullyConnectedWeights
    - @ref CLCopy / CLCopyKernel
    - @ref CLLSTMLayer
    - @ref CLRNNLayer
    - CLWidthConcatenateLayer / CLWidthConcatenateLayerKernel
    - CLWinogradFilterTransformKernel / @ref CLWinogradConvolutionLayer
    - CLWinogradInputTransformKernel / CLWinogradInputTransform
 - New Arm® Neon™ kernels / functions:
    - NEConvertFullyConnectedWeightsKernel / @ref NEConvertFullyConnectedWeights.
 - Created the validate method in @ref CLDepthwiseConvolutionLayer.
 - Beta and gamma are no longer mandatory arguments in @ref NEBatchNormalizationLayer and @ref CLBatchNormalizationLayer.
 - Added depth multiplier support in @ref NEDepthwiseConvolutionLayer and @ref CLDepthwiseConvolutionLayer.
 - Added broadcast multiply support in @ref NEPixelWiseMultiplication / NEPixelWiseMultiplicationKernel.
 - Port mobilenet example to NHWC data layout.
 - Enabled Winograd method in @ref CLConvolutionLayer.
 - Renamed NEWinogradLayer to @ref NEWinogradConvolutionLayer.
 - Updated @ref NEWinogradConvolutionLayer to use highly optimised assembly kernels in src/core/Neon/kernels/arm_gemm.
 - Added memory manager support in GLES functions.
 - Major refactoring of the graph API.
 - Added GLES backend in the graph API.
 - Added support for the memory manager in the graph API.
 - Enabled Winograd Convolution method in the graph API.
 - Added support for grouped convolutions in the graph API.
 - Replaced NEDeconvolutionLayerUpsampleKernel with NEScaleKernel in @ref NEDeconvolutionLayer.
 - Added fast maths flag in @ref CLConvolutionLayer.
 - Added new tests and benchmarks in validation and benchmark frameworks
 - Merge Activation layer with Convolution Layer (Neon™, CL, GLES)
 - Added support to OpenCL 2.0 SVM
 - Added support to import memory in OpenCL tensors.
 - Added the prepare() method to perform any one off pre-processing before running the function.
 - Added new examples:
    - graph_inception_v4.cpp
    - graph_resnext50.cpp
 - Added memory measurement instrument for CL.

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 Arm® 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:
    - CLDepthwiseConvolutionLayer3x3
    - @ref CLDepthwiseConvolutionLayer
 - Added broadcasting support to 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:
    - CLDirectConvolutionLayerOutputStageKernel
 - New Arm® Neon™ kernels / functions
    - Added name() method to all kernels.
    - Added support for Winograd 5x5.
    - NEPermuteKernel / @ref NEPermute
    - CpuWinogradConv2dTransformInputKernel / NEWinogradLayer
    - CpuWinogradConv2dTransformOutputKernel / NEWinogradLayer
    - CpuWinogradConv2dTransformWeightsKernel / NEWinogradLayer
    - Renamed NEWinogradLayerKernel into NEWinogradLayerBatchedGEMMKernel
 - New GLES kernels / functions:
    - GCTensorShiftKernel / GCTensorShift

v18.01 Public maintenance release
 - Various bug fixes
 - Added some of the missing validate() methods
 - Added CLDeconvolutionLayerUpsampleKernel / @ref CLDeconvolutionLayer @ref CLDeconvolutionLayerUpsample
 - Added CLPermuteKernel / @ref CLPermute
 - Added method to clean the programs cache in the CL Kernel library.
 - Added GCArithmeticAdditionKernel / GCArithmeticAddition
 - Added GCDepthwiseConvolutionLayer3x3Kernel / GCDepthwiseConvolutionLayer3x3
 - Added GCNormalizePlanarYUVLayerKernel / GCNormalizePlanarYUVLayer
 - Added GCScaleKernel / GCScale
 - Added GCWeightsReshapeKernel / GCConvolutionLayer
 - Added FP16 support to the following GLES compute kernels:
    - GCCol2ImKernel
    - GCGEMMInterleave4x4Kernel
    - GCGEMMTranspose1xWKernel
    - GCIm2ColKernel
 - Refactored Arm® Neon™ Winograd (NEWinogradLayerKernel)
 - Added NEDirectConvolutionLayerOutputStageKernel
 - Added QASYMM8 support to the following Arm® Neon™ kernels:
    - NEDepthwiseConvolutionLayer3x3Kernel
    - NEFillBorderKernel
    - 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 Arm® 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
    - GCAbsoluteDifferenceKernel / GCAbsoluteDifference
    - GCActivationLayerKernel / GCActivationLayer
    - GCBatchNormalizationLayerKernel / GCBatchNormalizationLayer
    - GCCol2ImKernel
    - GCDepthConcatenateLayerKernel / GCDepthConcatenateLayer
    - GCDirectConvolutionLayerKernel / GCDirectConvolutionLayer
    - GCDropoutLayerKernel / GCDropoutLayer
    - GCFillBorderKernel / GCFillBorder
    - GCGEMMInterleave4x4Kernel / GCGEMMInterleave4x4
    - GCGEMMMatrixAccumulateBiasesKernel / GCGEMMMatrixAdditionKernel / GCGEMMMatrixMultiplyKernel / GCGEMM
    - GCGEMMTranspose1xWKernel / GCGEMMTranspose1xW
    - GCIm2ColKernel
    - GCNormalizationLayerKernel / GCNormalizationLayer
    - GCPixelWiseMultiplicationKernel / GCPixelWiseMultiplication
    - GCPoolingLayerKernel / GCPoolingLayer
    - GCLogits1DMaxKernel / GCLogits1DShiftExpSumKernel / GCLogits1DNormKernel / GCSoftmaxLayer
    - GCTransposeKernel / GCTranspose

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

 - New OpenCL kernels / functions
    - CLGEMMLowpOffsetContributionKernel / CLGEMMLowpMatrixAReductionKernel / CLGEMMLowpMatrixBReductionKernel / CLGEMMLowpMatrixMultiplyCore
    - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel / CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint

 - New graph nodes for Arm® 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 Arm® Neon™ and OpenCL.
 - New Arm® Neon™ kernels / functions:
    - arm_compute::NEGEMMAssemblyBaseKernel arm_compute::NEGEMMAArch64Kernel
    - NEDequantizationLayerKernel / @ref NEDequantizationLayer
    - NEFloorKernel / @ref NEFloor
    - NEL2NormalizeLayerKernel / @ref NEL2NormalizeLayer
    - NEQuantizationLayerKernel NEMinMaxLayerKernel / @ref NEQuantizationLayer
    - NEROIPoolingLayerKernel / @ref NEROIPoolingLayer
    - NEReductionOperationKernel / @ref NEReductionOperation
    - NEReshapeLayerKernel / @ref NEReshapeLayer

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

v17.06 Public major release
 - Various bug fixes
 - Added support for fixed point 8 bit (QS8) to the various Arm® 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 Arm® Neon™ (For bare metal)
 - User can specify their own scheduler by implementing the @ref IScheduler interface.
 - New OpenCL kernels / functions:
    - CLBatchNormalizationLayerKernel / @ref CLBatchNormalizationLayer
    - CLDepthConcatenateLayerKernel / CLDepthConcatenateLayer
    - CLHOGOrientationBinningKernel CLHOGBlockNormalizationKernel, CLHOGDetectorKernel / CLHOGDescriptor CLHOGDetector CLHOGGradient CLHOGMultiDetection
    - CLLocallyConnectedMatrixMultiplyKernel / CLLocallyConnectedLayer
    - CLWeightsReshapeKernel / CLConvolutionLayerReshapeWeights
 - New C++ kernels:
    - CPPDetectionWindowNonMaximaSuppressionKernel
 - New Arm® Neon™ kernels / functions:
    - NEBatchNormalizationLayerKernel / @ref NEBatchNormalizationLayer
    - NEDepthConcatenateLayerKernel / NEDepthConcatenateLayer
    - NEDirectConvolutionLayerKernel / @ref NEDirectConvolutionLayer
    - NELocallyConnectedMatrixMultiplyKernel / NELocallyConnectedLayer
    - NEWeightsReshapeKernel / 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:
 -  CLColorConvertKernel
 -  CLEdgeNonMaxSuppressionKernel
 -  CLEdgeTraceKernel
 -  CLGaussianPyramidHorKernel
 -  CLGaussianPyramidVertKernel
 -  CLGradientKernel
 -  NEChannelCombineKernel
 -  NEFillArrayKernel
 -  NEGaussianPyramidHorKernel
 -  NEGaussianPyramidVertKernel
 -  NEHarrisScoreFP16Kernel
 -  NEHarrisScoreKernel
 -  NEHOGDetectorKernel
 -  NELogits1DMaxKernel
 -  NELogits1DShiftExpSumKernel
 -  NELogits1DNormKernel
 -  NENonMaximaSuppression3x3FP16Kernel
 -  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 Arm® Neon™ and CL functions.
 - New padding calculation interface introduced and ported most kernels / functions to use it.
 - New OpenCL kernels / functions:
   - CLGEMMLowpMatrixMultiplyKernel / CLGEMMLowp
 - New Arm® Neon™ kernels / functions:
   - NENormalizationLayerKernel / @ref NENormalizationLayer
   - NETransposeKernel / @ref NETranspose
   - NELogits1DMaxKernel, NELogits1DShiftExpSumKernel, NELogits1DNormKernel / @ref NESoftmaxLayer
   - NEIm2ColKernel, NECol2ImKernel, NEConvolutionLayerWeightsReshapeKernel / @ref NEConvolutionLayer
   - NEGEMMMatrixAccumulateBiasesKernel / @ref NEFullyConnectedLayer
   - NEGEMMLowpMatrixMultiplyKernel / NEGEMMLowp

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

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

v17.02 Sources preview
 - New OpenCL kernels / functions:
   - CLActivationLayerKernel / @ref CLActivationLayer
   - CLChannelCombineKernel / CLChannelCombine
   - CLDerivativeKernel / CLChannelExtract
   - CLFastCornersKernel / CLFastCorners
   - CLMeanStdDevKernel / CLMeanStdDev
 - New Arm® Neon™ kernels / functions:
   - HOG / SVM: NEHOGOrientationBinningKernel, NEHOGBlockNormalizationKernel, NEHOGDetectorKernel, NEHOGNonMaximaSuppressionKernel / NEHOGDescriptor, NEHOGDetector, NEHOGGradient, NEHOGMultiDetection
   - NENonLinearFilterKernel / 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

 */
} // namespace arm_compute
