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| author | Michele Di Giorgio <michele.digiorgio@arm.com> | 2021-03-09 14:09:08 +0000 |
|---|---|---|
| committer | Michele Di Giorgio <michele.digiorgio@arm.com> | 2021-03-31 17:08:51 +0000 |
| commit | 33f41fabd30fb444aaa0cf3e65b61794d498d151 (patch) | |
| tree | a381cff3096a3b05198b0cd311fee28e40fd5a4f /src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h | |
| parent | 5f91b5d7063462854b62d342f9d4e04ae647e9a6 (diff) | |
| download | ComputeLibrary-33f41fabd30fb444aaa0cf3e65b61794d498d151.tar.gz | |
Fix trademarks throughout the codebase
Resolves: COMPMID-4299
Change-Id: Ie6a52c1371b9a2a7b5bb4f019ecd5e70a2008567
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5338
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h')
| -rw-r--r-- | src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h index e2945ee117..3bc162a1b4 100644 --- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h +++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h @@ -30,7 +30,7 @@ namespace arm_compute { class ITensor; -/** Neon kernel to multiply two input matrices "A" and "B". All elements of the output matrix/vector will be multiplied by alpha after the matrix multiplication +/** Kernel to multiply two input matrices "A" and "B". All elements of the output matrix/vector will be multiplied by alpha after the matrix multiplication * * @note If the output tensor is a matrix, the implementation assumes that the input tensors @p input0 and @p input1 are both matrices and reshaped respectively with @ref NEGEMMInterleave4x4Kernel" and @ref NEGEMMTranspose1xWKernel * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p input0 is a vector and the second input tensor @p input1 a matrix. The implementation also assumes that both tensors have not been reshaped |