Add Gemm MMUL Reshaped Only Rhs Support for FP32/FP16

This patch introduces a GEMM routine that is optimized for Arm(R) Mali(TM)-G715 and Arm(R) Mali(TM)-G615

Resolves: COMPMID-5216
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Change-Id: I2e5d7806f5904347185bb3e250f73d73d6669dba
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7914
Reviewed-by: SiCong Li <sicong.li@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 588 insertions, 11 deletions

diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp
index 5172a7730a..94675d60cc 100644
--- a/src/core/CL/CLHelpers.cpp
+++ b/src/core/CL/CLHelpers.cpp
@@ -491,4 +491,8 @@ void set_unroll_with_pragma(CLBuildOptions &built_opts, std::initializer_list<in
     }
 }
 
+bool arm_matrix_multiply_supported(const cl::Device &device)
+{
+    return device_supports_extension(device, "cl_arm_matrix_multiply");
+}
 } // namespace arm_compute
diff --git a/src/core/CL/cl_kernels/common/gemm_reshaped_only_rhs_mmul.cl b/src/core/CL/cl_kernels/common/gemm_reshaped_only_rhs_mmul.cl
new file mode 100644
index 0000000000..8919023d4c
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/gemm_reshaped_only_rhs_mmul.cl
@@ -0,0 +1,528 @@
+/*
+ * Copyright (c) 2022 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "activation_float_helpers.h"
+#include "helpers.h"
+#include "tile_helpers.h"
+
+#if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL)
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension:
+ *
+ *  The LHS matrix is NOT reshaped
+ *  The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed
+ *
+ * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4).
+ * @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2)
+ * @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2)
+ * @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2)
+ * @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ *  - M0 > 0
+ *  - N0 = 1, 2, 3, 4, 8, 16
+ *  - K0 = 1
+ *
+ * @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively.
+ *       The activation function is performed after the bias addition
+ *
+ * @param[in]  lhs_ptr                           Pointer to the LHS tensor. Supported data types: F16/F32
+ * @param[in]  lhs_stride_y                      Stride of the LHS tensor in Y dimension (in bytes)
+ * @param[in]  lhs_stride_z                      Stride of the LHS tensor in Z dimension (in bytes)
+ * @param[in]  lhs_w                             The size of the width dimension of the LHS tensor
+ * @param[in]  lhs_h                             The size of the height dimension of the LHS tensor
+ * @param[in]  lhs_n                             The size of the depth dimension of the LHS tensor
+ * @param[in]  lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor
+ * @param[in]  rhs_ptr                           Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  rhs_stride_y                      Stride of the RHS tensor in Y dimension (in bytes)
+ * @param[in]  rhs_stride_z                      Stride of the RHS tensor in Z dimension (in bytes)
+ * @param[in]  rhs_w                             The size of the width dimension of the RHS tensor
+ * @param[in]  rhs_h                             The size of the height dimension of the RHS tensor
+ * @param[in]  rhs_n                             The size of the depth dimension of the RHS tensor
+ * @param[in]  rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor
+ * @param[in]  bia_ptr                           (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  bia_stride_y                      (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in]  bia_stride_z                      (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in]  bia_w                             (Optional) The size of the width dimension of the bias tensor
+ * @param[in]  bia_h                             (Optional) The size of the height dimension of the bias tensor
+ * @param[in]  bia_n                             (Optional) The size of the depth dimension of the bias tensor
+ * @param[in]  bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_w                             The size of the width dimension of the destination tensor
+ * @param[in]  dst_h                             The size of the height dimension of the destination tensor
+ * @param[in]  dst_n                             The size of the depth dimension of the destination tensor
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in]  M                                 Number of rows in LHS matrix not reshaped
+ * @param[in]  N                                 Number of columns in RHS matrix not reshaped
+ * @param[in]  K                                 Number of columns in LHS matrix and rows in RHS matrix not reshaped
+ */
+__kernel void gemm_mm_reshaped_only_rhs_nt_mmul(
+    TENSOR3D_T(lhs, BUFFER),
+    TENSOR3D_T(rhs, BUFFER),
+#if defined(BETA)
+    TENSOR3D_T(bia, BUFFER),
+#endif // defined(BETA)
+    TENSOR3D_T(dst, BUFFER),
+    const int M,
+    const int N,
+    const int K)
+{
+#define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0)
+
+    uint x0 = get_global_id(0); // (N / N0) * MMUL_K0
+    uint y0 = get_global_id(1); // (M / M0) / MMUL_M0
+    uint z  = get_global_id(2); // Batch
+
+    // Get block ID and thread ID within the block
+    uint block_id  = (x0 / MMUL_BLOCK_SIZE);
+    uint thread_id = (x0 % MMUL_BLOCK_SIZE);
+
+    // Coordinate within a block
+    uint block_x = thread_id % MMUL_N0;
+    uint block_y = (thread_id / MMUL_M0);
+
+    // Starting destination coordinates
+    uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1));
+    uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0));
+
+    // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication
+    // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results
+
+    // Starting LHS coordinates
+    uint lhs_x = block_x;
+    uint lhs_y = dst_y;
+
+    // Starting RHS coordinates
+    uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0;
+    uint rhs_y = block_id;
+
+    // Compute LHS/RHS/DST matrix address
+    lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z;
+    rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z;
+    dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z;
+
+    // Note: If RHS derives from the weights of convolution 2d layer, RHS will always be 2D and rhs_stride_z will always be equal to 0 for
+    // not sliding the tensor
+
+    // Initialize the accumulators
+    // MMUL extension accumulate the result in F32 for both F32 and F16
+    TILE(float, M0, N0, c_f32);
+
+#if !defined(HALF_PRECISION)
+#define c c_f32
+#endif // !defined(HALF_PRECISION)
+
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        c_f32[i].v = 0;
+    })
+
+    for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0)
+    {
+        TILE(DATA_TYPE, M0, 1, a);
+        TILE(DATA_TYPE, 1, N0, b);
+
+        // Load tile from the lhs/rhs tensors
+        T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+        T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, 0, b);
+
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            LOOP_UNROLLING(int, n0, 0, 1, N0,
+            {
+                c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]);
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE);
+        rhs_offset_first_element_in_bytes += MMUL_K0 * MMUL_N0 * N0 * sizeof(DATA_TYPE);
+    }
+
+    if(block_x * N0 + block_id * MMUL_N0 * N0 >= N)
+    {
+        return;
+    }
+
+    if(block_y * M0 + y0 * M0 * MMUL_M0 >= M)
+    {
+        return;
+    }
+
+#if defined(HALF_PRECISION)
+    TILE(DATA_TYPE, M0, N0, c);
+
+    // Conversion required for the half precision
+    LOOP_UNROLLING(int, m0, 0, 1, M0,
+    {
+        LOOP_UNROLLING(int, n0, 0, 1, N0,
+        {
+            c[m0].s[n0] = c_f32[m0].s[n0];
+        })
+    })
+#endif // defined(HALF_PRECISION)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE);
+
+    TILE(DATA_TYPE, 1, N0, bias0);
+
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
+    }
+    else
+    {
+        VLOAD_PARTIAL(N0, N0_LEFTOVER)
+        (bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
+    }
+
+#ifndef UNIT_BETA
+    T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c);
+#else // defined(BROADCAST_BIAS)
+    TILE(DATA_TYPE, M0, N0, bias0);
+
+    bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z;
+
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
+            }
+        })
+    }
+    else
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VLOAD_PARTIAL(N0, N0_LEFTOVER)
+                (bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
+            }
+        })
+    }
+
+#ifndef UNIT_BETA
+    T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    T_ADD(DATA_TYPE, M0, N0, c, bias0, c);
+    // c = c + bias
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c);
+
+    // Store
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VSTORE(N0)
+                (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+            }
+        })
+    }
+    else
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VSTORE_PARTIAL(N0, N0_LEFTOVER)
+                (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+            }
+        })
+    }
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+#endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL)
+
+#if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL_TEXTURE)
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension and the OpenCL image for RHS:
+ *
+ *  The LHS matrix is NOT reshaped
+ *  The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed
+ *
+ * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4).
+ * @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2)
+ * @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2)
+ * @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2)
+ * @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ *  - M0 > 0
+ *  - N0 = 1, 2, 3, 4, 8, 16
+ *  - K0 = 1
+ *
+ * @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively.
+ *       The activation function is performed after the bias addition
+ *
+ * @param[in]  lhs_ptr                           Pointer to the LHS tensor. Supported data types: F16/F32
+ * @param[in]  lhs_stride_y                      Stride of the LHS tensor in Y dimension (in bytes)
+ * @param[in]  lhs_stride_z                      Stride of the LHS tensor in Z dimension (in bytes)
+ * @param[in]  lhs_w                             The size of the width dimension of the LHS tensor
+ * @param[in]  lhs_h                             The size of the height dimension of the LHS tensor
+ * @param[in]  lhs_n                             The size of the depth dimension of the LHS tensor
+ * @param[in]  lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor
+ * @param[in]  rhs_ptr                           Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  rhs_stride_y                      Stride of the RHS tensor in Y dimension (in bytes)
+ * @param[in]  rhs_stride_z                      Stride of the RHS tensor in Z dimension (in bytes)
+ * @param[in]  rhs_w                             The size of the width dimension of the RHS tensor
+ * @param[in]  rhs_h                             The size of the height dimension of the RHS tensor
+ * @param[in]  rhs_n                             The size of the depth dimension of the RHS tensor
+ * @param[in]  rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor
+ * @param[in]  bia_ptr                           (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  bia_stride_y                      (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in]  bia_stride_z                      (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in]  bia_w                             (Optional) The size of the width dimension of the bias tensor
+ * @param[in]  bia_h                             (Optional) The size of the height dimension of the bias tensor
+ * @param[in]  bia_n                             (Optional) The size of the depth dimension of the bias tensor
+ * @param[in]  bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data type: same as @p lhs_ptr
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_w                             The size of the width dimension of the destination tensor
+ * @param[in]  dst_h                             The size of the height dimension of the destination tensor
+ * @param[in]  dst_n                             The size of the depth dimension of the destination tensor
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in]  M                                 Number of rows in LHS matrix not reshaped
+ * @param[in]  N                                 Number of columns in RHS matrix not reshaped
+ * @param[in]  K                                 Number of columns in LHS matrix and rows in RHS matrix not reshaped
+ */
+__kernel void gemm_mm_reshaped_only_rhs_nt_mmul_texture(
+    TENSOR3D_T(lhs, BUFFER),
+    TENSOR3D_T(rhs, IMAGE),
+#if defined(BETA)
+    TENSOR3D_T(bia, BUFFER),
+#endif // defined(BETA)
+    TENSOR3D_T(dst, BUFFER),
+    const int M,
+    const int N,
+    const int K)
+{
+#define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0)
+
+    uint x0 = get_global_id(0); // (N / N0) * MMUL_K0
+    uint y0 = get_global_id(1); // (M / M0) / MMUL_M0
+    uint z  = get_global_id(2); // Batch
+
+    // Get block ID and thread ID within the block
+    uint block_id  = (x0 / MMUL_BLOCK_SIZE);
+    uint thread_id = (x0 % MMUL_BLOCK_SIZE);
+
+    // Coordinate within a block
+    uint block_x = thread_id % MMUL_N0;
+    uint block_y = (thread_id / MMUL_M0);
+
+    // Starting destination coordinates
+    uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1));
+    uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0));
+
+    // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication
+    // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results
+
+    // Starting LHS coordinates
+    uint lhs_x = block_x;
+    uint lhs_y = dst_y;
+
+    // Starting RHS coordinates
+    uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0;
+    uint rhs_y = block_id + z * rhs_h;
+
+    // Compute LHS/RHS/DST matrix address
+    lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z;
+    dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z;
+
+    // Initialize the accumulators
+    // MMUL extension accumulate the result in F32 for both F32 and F16
+    TILE(float, M0, N0, c_f32);
+
+#if !defined(HALF_PRECISION)
+#define c c_f32
+#endif // !defined(HALF_PRECISION)
+
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        c_f32[i].v = 0;
+    })
+
+    for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0)
+    {
+        TILE(DATA_TYPE, M0, 1, a);
+        TILE(DATA_TYPE, 1, N0, b);
+
+        // Load tile from the lhs/rhs tensors
+        T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+        T_LOAD(DATA_TYPE, 1, N0, IMAGE, rhs, rhs_x, rhs_y, 1, rhs_stride_y, b);
+
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            LOOP_UNROLLING(int, n0, 0, 1, N0,
+            {
+                c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]);
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE);
+        rhs_x += MMUL_K0 * MMUL_N0 * N0;
+    }
+
+    if(block_x * N0 + block_id * MMUL_N0 * N0 >= N)
+    {
+        return;
+    }
+
+    if(block_y * M0 + y0 * M0 * MMUL_M0 >= M)
+    {
+        return;
+    }
+
+#if defined(HALF_PRECISION)
+    TILE(DATA_TYPE, M0, N0, c);
+
+    // Conversion required for the half precision
+    LOOP_UNROLLING(int, m0, 0, 1, M0,
+    {
+        LOOP_UNROLLING(int, n0, 0, 1, N0,
+        {
+            c[m0].s[n0] = c_f32[m0].s[n0];
+        })
+    })
+#endif // defined(HALF_PRECISION)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE);
+
+    TILE(DATA_TYPE, 1, N0, bias0);
+
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
+    }
+    else
+    {
+        VLOAD_PARTIAL(N0, N0_LEFTOVER)
+        (bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
+    }
+
+#ifndef UNIT_BETA
+    T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c);
+#else // defined(BROADCAST_BIAS)
+    TILE(DATA_TYPE, M0, N0, bias0);
+
+    bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z;
+
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
+            }
+        })
+    }
+    else
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VLOAD_PARTIAL(N0, N0_LEFTOVER)
+                (bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
+            }
+        })
+    }
+
+#ifndef UNIT_BETA
+    T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    T_ADD(DATA_TYPE, M0, N0, c, bias0, c);
+    // c = c + bias
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c);
+
+    // Store
+    if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VSTORE(N0)
+                (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+            }
+        })
+    }
+    else
+    {
+        LOOP_UNROLLING(int, m0, 0, 1, M0,
+        {
+            if(dst_y + m0 < M || M0_LEFTOVER == 0)
+            {
+                VSTORE_PARTIAL(N0, N0_LEFTOVER)
+                (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+            }
+        })
+    }
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+#endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL_TEXTURE)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h
index 0ce343e3ec..4b6144a22d 100644
--- a/src/core/CL/cl_kernels/tile_helpers.h
+++ b/src/core/CL/cl_kernels/tile_helpers.h
@@ -970,8 +970,8 @@
 #define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x)
 #define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x)
 
-#define T_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL))
-#define T_DIV(A_VAL, B_VAL) ((A_VAL) / (B_VAL))
+#define V_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL))
+#define V_DIV(A_VAL, B_VAL) ((A_VAL) / (B_VAL))
 
 /** Element-wise activation for quantized types
  *
@@ -995,6 +995,25 @@
         })                                                                                          \
     })
 
+/** Element-wise addition between two tiles
+ *
+ * @note Performs: LHS + RHS = DST
+ *
+ * @param[in]  DATA_TYPE LHS/RHS/DST data type
+ * @param[in]  M0        Number of LHS rows
+ * @param[in]  N0        Number of LHS columns
+ * @param[in]  lhs       LHS tile
+ * @param[in]  rhs       Constant RHS tile
+ * @param[out] dst       DST tile
+ */
+#define T_ADD(DATA_TYPE, M0, N0, lhs, rhs, dst) \
+    ({                                                            \
+        LOOP_UNROLLING(int, _m0, 0, 1, M0,                        \
+        {                                                         \
+            dst[_m0].v = lhs[_m0].v + rhs[_m0].v; \
+        })                                                        \
+    })
+
 /** Element-wise addition with a constant value
  *
  * @note Performs: LHS + constant = DST
@@ -1010,15 +1029,31 @@
     ({                                                            \
         LOOP_UNROLLING(int, _m0, 0, 1, M0,                        \
         {                                                         \
-            LOOP_UNROLLING(int, _n0, 0, 1, N0,                    \
-            {                                                     \
-                dst[_m0].s[_n0] = lhs[_m0].s[_n0] + rhs_constant; \
-            })                                                    \
+            dst[_m0].v = lhs[_m0].v + (DATA_TYPE)rhs_constant;               \
         })                                                        \
     })
 
-#define T_ELTWISE_BROADCAST_ADD_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(T_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
-#define T_ELTWISE_BROADCAST_DIV_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(T_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
+#define T_ELTWISE_BROADCAST_ADD_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
+#define T_ELTWISE_BROADCAST_DIV_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE_BROADCAST_X(V_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
+
+/** Element-wise scale with a constant value
+ *
+ * @note Performs: LHS * constant = DST
+ *
+ * @param[in]  DATA_TYPE    LHS/RHS/DST data type
+ * @param[in]  M0           Number of LHS rows
+ * @param[in]  N0           Number of LHS columns
+ * @param[in]  lhs          LHS tile
+ * @param[in]  rhs_constant Constant value
+ * @param[out] dst          DST tile
+ */
+#define T_SCALE_CONSTANT(DATA_TYPE, M0, N0, lhs, rhs_constant, dst) \
+    ({                                                            \
+        LOOP_UNROLLING(int, _m0, 0, 1, M0,                        \
+        {                                                         \
+            dst[_m0].v = lhs[_m0].v * (DATA_TYPE)rhs_constant; \
+        })                                                        \
+    })
 
 /** Element-wise operation with RHS broadcasted (RHS has the X dimension only)
  *
@@ -1041,8 +1076,8 @@
         })                                                  \
     })
 
-#define T_ELTWISE_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(T_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
-#define T_ELTWISE_DIV(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(T_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
+#define T_ELTWISE_ADD(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_ADD, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
+#define T_ELTWISE_DIV(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) T_ELTWISE(V_DIV, DST_DATA_TYPE, M0, N0, lhs, rhs, dst)
 
 /** Element-wise operation between two tiles (LHS and RHS)
  *
diff --git a/src/core/GPUTarget.cpp b/src/core/GPUTarget.cpp
index 5984c88099..e74abf6e7e 100644
--- a/src/core/GPUTarget.cpp
+++ b/src/core/GPUTarget.cpp
@@ -47,6 +47,14 @@ arm_compute::GPUTarget get_valhall_target(const std::string &version)
     {
         return arm_compute::GPUTarget::G57;
     }
+    else if(version.find("G715") != std::string::npos)
+    {
+        return arm_compute::GPUTarget::G715;
+    }
+    else if(version.find("G615") != std::string::npos)
+    {
+        return arm_compute::GPUTarget::G615;
+    }
     else
     {
         return arm_compute::GPUTarget::UNKNOWN;
@@ -141,7 +149,9 @@ const std::string &string_from_target(GPUTarget target)
         { GPUTarget::G77, "g77" },
         { GPUTarget::G78, "g78" },
         { GPUTarget::G710, "g710" },
-        { GPUTarget::G57, "g57" }
+        { GPUTarget::G57, "g57" },
+        { GPUTarget::G715, "g715" },
+        { GPUTarget::G615, "g615" }
     };
 
     return gpu_target_map[target];