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| author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2018-04-11 15:59:10 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:37 +0000 |
| commit | e52a3000d2c13bc1b66ca66b3d12b6b836982394 (patch) | |
| tree | 70e8ef5ba216762604f84228805aac9bd65747b6 /arm_compute/runtime/CL/functions/CLWinogradInputTransform.h | |
| parent | dd03870b63784abe499761da2b26b209b33f2db2 (diff) | |
| download | ComputeLibrary-e52a3000d2c13bc1b66ca66b3d12b6b836982394.tar.gz | |
COMPMID-1026 - Add support for 4x4 output tile in CLWinogradConvolutionLayer
The performance achieved can be found at the following confluence page:
https://confluence.arm.com/display/MLENG/GEMM-based+convolution+vs+Winograd-based+convolution+on+OpenCL
Change-Id: I4b690cfdd4eb4ff0cd17b14fdd49ccaa1d1dc85c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127729
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'arm_compute/runtime/CL/functions/CLWinogradInputTransform.h')
| -rw-r--r-- | arm_compute/runtime/CL/functions/CLWinogradInputTransform.h | 6 |
1 files changed, 2 insertions, 4 deletions
diff --git a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h index 0e0d6bf284..64d3e80bc9 100644 --- a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h +++ b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h @@ -40,8 +40,7 @@ public: /** Set the input and output tensors. * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 @@ -52,8 +51,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransform. * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 |