diff options
| author | Moritz Pflanzer <moritz.pflanzer@arm.com> | 2017-09-15 10:42:58 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
| commit | 80373f607cb12693824411510c39e367a4dfbdb5 (patch) | |
| tree | ddc4d038783ed91ff227fb259a85fefc09e46319 /src/runtime/NEON/functions/NEConvolutionLayer.cpp | |
| parent | c09314a288dc2aa7ef75a09a8ff5dede3f80974a (diff) | |
| download | ComputeLibrary-80373f607cb12693824411510c39e367a4dfbdb5.tar.gz | |
COMPMID-481: Add AArch32 GEMM
Change-Id: Idba0b30bfb27866a46a22388014ab81432ea28dc
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86196
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Diffstat (limited to 'src/runtime/NEON/functions/NEConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEConvolutionLayer.cpp | 19 |
1 files changed, 15 insertions, 4 deletions
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index 44bf2de70c..cbe3b65c34 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -23,6 +23,7 @@ */ #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" +#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h" #include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Size2D.h" @@ -34,6 +35,7 @@ namespace arm_compute { #include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp" +#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp" #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp" } // namespace arm_compute @@ -151,12 +153,17 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, // Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); -#if defined(__aarch64__) +#if defined(__arm__) + if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32) + { + _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>(); + } +#elif defined(__aarch64__) if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32) { _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>(); } -#endif /* defined(__aarch64__) */ +#endif /* defined(__arm__) || defined(__aarch64__) */ unsigned int mat_weights_cols = weights->info()->dimension(3); unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); @@ -240,7 +247,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, // Configure kernels _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); -#if defined(__aarch64__) +#if defined(__arm__) || defined(__aarch64__) if(_mm_optimised_kernel != nullptr) { struct CPUInfo ci = NEScheduler::get().cpu_info(); @@ -249,7 +256,11 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const int N = _gemm_output.info()->tensor_shape().x(); const int K = _input_im2col_reshaped.info()->tensor_shape().x(); +#if defined(__arm__) + GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false); +#elif defined(__aarch64__) GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false); +#endif /* defined(__arm__) || defined(__aarch64__) */ constexpr size_t alignment = 4096; _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); @@ -268,7 +279,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, _workspace.allocator()->allocate(); } else -#endif /* defined(__aarch64__) */ +#endif /* defined(__arm__) || defined(__aarch64__) */ { if(_is_fully_connected_convolution) { |