COMPMID-3237: Extend GEMMLowpReduction kernels to multiply reductions by a scalar value

Change-Id: If2a242f52aea753591525d30a4cb64c1a766bf8d
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2881
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 7 insertions, 7 deletions

diff --git a/arm_compute/runtime/NEON/functions/NEGEMM.h b/arm_compute/runtime/NEON/functions/NEGEMM.h
index c87e806d0c..8dc6b88bb0 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMM.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMM.h
@@ -74,7 +74,7 @@ public:
      * @note GEMM: General Matrix Multiply - [alpha * A * B + beta * C].
      * @note GEMM: The tensors a, b, c, d must have the same data type. You should not mix data types when calling this function.
      *
-     * @param[in]  a         First input tensor  (Matrix A or Vector A). Data type supported: BLOAT16/F16/F32
+     * @param[in]  a         First input tensor  (Matrix A or Vector A). Data type supported: BFLOAT16/F16/F32
      * @param[in]  b         Second input tensor (Matrix B). Data type supported: same as @p a
      * @param[in]  c         Third input tensor  (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a
      * @param[out] d         Output tensor. Data type supported: same as @p a
@@ -86,7 +86,7 @@ public:
     void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo());
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMM.
      *
-     * @param[in]  a         First input tensor info  (Matrix or Vector A). Data types supported: BLOAT16/F16/F32
+     * @param[in]  a         First input tensor info  (Matrix or Vector A). Data types supported: BFLOAT16/F16/F32
      * @param[in]  b         Second input tensor info (Matrix B). Data type supported: same as @p a.
      * @param[in]  c         Third input tensor info  (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a.
      * @param[out] output    Output tensor info. Data type supported: same as @p a
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
index 5368384b19..e7da1006e0 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
@@ -66,9 +66,9 @@ public:
      * @param[in]  weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                     Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @param[in]  biases  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
-     *                     Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED, FP32 if @p weights is BLOAT16
+     *                     Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED, FP32 if @p weights is BFLOAT16
      * @param[out] output  Destination tensor.
-     *                     Data types supported: Same as @p weights, FP32 if @p weights is BLOAT16
+     *                     Data types supported: Same as @p weights, FP32 if @p weights is BFLOAT16
      */
     void configure(const ITensor *weights, const ITensor *biases, ITensor *output);
     /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
@@ -76,9 +76,9 @@ public:
      * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                    Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @param[in] biases  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
-     *                    Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED, FP32 if @p weights is BLOAT16
+     *                    Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED, FP32 if @p weights is BFLOAT16
      * @param[in] output  Destination tensor.
-     *                    Data types supported: Same as @p weights FP32 if @p weights is BLOAT16
+     *                    Data types supported: Same as @p weights FP32 if @p weights is BFLOAT16
      *
      * @return an error status
      */
@@ -140,7 +140,7 @@ private:
 /** Basic function to compute the convolution layer. This function calls the following NEON kernels/functions:
  *
  * -# @ref NEIm2ColKernel
- * -# @ref NEGEMM (if the data type is BLOAT16/FP16/FP32)
+ * -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32)
  * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED)
  * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED)
  * -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)