diff --git a/Android.bp b/Android.bp
index 4ec0475..32d5805 100644
--- a/Android.bp
+++ b/Android.bp
@@ -28,7 +28,9 @@
         "src/core/CL/cl_kernels/common/elementwise_operation_quantized.cl",
         "src/core/CL/cl_kernels/common/elementwise_unary.cl",
         "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/fp_post_ops_act_eltwise_op_act.h",
+        "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl",
         "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl",
+        "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl",
         "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/fp_elementwise_op_helpers.h",
         "src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/fp_mixed_precision_helpers.h",
         "src/core/CL/cl_kernels/common/fft.cl",
diff --git a/SConscript b/SConscript
index 468d738..c5c6ca3 100644
--- a/SConscript
+++ b/SConscript
@@ -310,7 +310,9 @@
                        'src/core/CL/cl_kernels/common/floor.cl',
                        'src/core/CL/cl_kernels/common/gather.cl',
                        'src/core/CL/cl_kernels/common/gemm.cl',
+                       'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl',
                        'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl',
+                       'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl',
                        'src/core/CL/cl_kernels/common/gemv.cl',
                        'src/core/CL/cl_kernels/common/gemmlowp.cl',
                        'src/core/CL/cl_kernels/common/generate_proposals.cl',
diff --git a/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl
new file mode 100644
index 0000000..e53ce3d
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl
@@ -0,0 +1,366 @@
+/*
+ * Copyright (c) 2021 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 "fp_post_ops_act_eltwise_op_act.h"
+#include "gemm_helpers.h"
+#include "repeat.h"
+
+/** (EXPERIMENTAL_POST_OPS) gemm_mm_native kernel */
+#if defined(M0) && defined(N0) && defined(K0) && defined(K) && defined(DATA_TYPE) && defined(PARTIAL_STORE_M0) && defined(PARTIAL_STORE_N0)
+#if defined(P2_ELTWISE_OP) && defined(P2_ELTWISE_ARG1_HEIGHT) && defined(P2_ELTWISE_ARG1_WIDTH)
+
+#define VFMA(a, b, c)     \
+    ({                    \
+        c = fma(a, b, c); \
+    })
+
+#if M0 == 1
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+    })
+#elif M0 == 2 // M0 == 2
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+    })
+#elif M0 == 3 // M0 == 3
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+    })
+#elif M0 == 4 // M0 == 4
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+    })
+#elif M0 == 5 // M0 == 5
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+    })
+#elif M0 == 6 // M0 == 6
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+    })
+#elif M0 == 7 // M0 == 7
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+    })
+#elif M0 == 8 // M0 == 8
+#define RHS_VFMA_M0xN0(i, a, b, c)                                    \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##7).s##i), b, (c##7)); \
+    })
+#else // M0 not supported
+#error "M0 not supported"
+#endif // M0 not supported
+
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices plus 3 post ops:
+ * Post op 1: activation (optional)
+ * Post op 2: elementwise op
+ * Post op 3: activation (optional)
+ *
+ * @note (Optional) -DP1_ACTIVATION_TYPE, -DP1_ACTIVATION_A_VAL, -DP1_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ * @note (Required) -DP2_ELTWISE_OP: The (binary) elementwise post op to perform
+ * @note (Required) -DP2_ELTWISE_ARG1_HEIGHT: The height (Y dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Required) -DP2_ELTWISE_ARG1_WIDTH: The width (X dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Optional) -DP3_ACTIVATION_TYPE, -DP3_ACTIVATION_A_VAL, -DP3_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ *
+ * All parameters are similarly defined in kernel gemm_mm_native, with these additions:
+ *
+ * @param[in] eltwise_operand_ptr      Pointer to the eltwise operand matrix. Supported data type: F16/F32
+ * @param[in] eltwise_operand_stride_x Stride of the eltwise operand matrix in X dimension (in bytes)
+ * @param[in] eltwise_operand_step_x   eltwise_operand_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_y Stride of the eltwise operand matrix in Y dimension (in bytes)
+ * @param[in] eltwise_operand_step_y   eltwise_operand_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_z Stride of the eltwise operand tensor in Z dimension (in bytes)
+ */
+__kernel void gemm_mm_native_post_act_eltwise_op_act(IMAGE_DECLARATION(lhs),
+                                                     IMAGE_DECLARATION(rhs),
+#if defined(BETA)
+                                                     IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
+                                                     IMAGE_DECLARATION(dst),
+                                                     // Post-Op arguments
+                                                     IMAGE_DECLARATION(eltwise_operand),
+                                                     uint lhs_stride_z,
+                                                     uint rhs_stride_z,
+#if defined(BETA)
+                                                     uint bias_stride_z,
+#endif //defined(BETA)
+                                                     uint dst_stride_z,
+                                                     uint eltwise_operand_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                                     ,
+                                                     uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                     ,
+                                                     uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                    )
+{
+    // Block size
+#define RHS_BLOCK_SIZE ((K0) * (N0))
+
+    // RHS offset and step X
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+
+    uint x = get_global_id(0);
+    uint y = get_global_id(1);
+    uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+    if((x * N0 >= N) || (y * M0 >= M))
+    {
+        return;
+    }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+    // Compute LHS matrix address
+    uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+    // Compute RHS matrix address
+    uint rhs_offset = rhs_offset_first_element_in_bytes + x * N0 * sizeof(DATA_TYPE);
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    rhs_offset += z * rhs_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zlhs, 0);
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zlhs, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply lhs_stride_z by DEPTH_GEMM3D
+    lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Initialize the accumulators
+    REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0)    c0=0,c1=0,c2=0,... c(M0-1)=0;
+
+    int i = 0;
+#if K0 > 1
+    for(; i <= (K - K0); i += K0)
+    {
+        // Supported cases (M0, K0):
+        // 1,2 - 1,3 - 1,4 - 1,8 - 1,16
+        // 2,2 - 2,3 - 2,4 - 2,8 - 2,16
+        // 3,2 - 3,3 - 3,4 - 3,8 - 3,16
+        // 4,2 - 4,3 - 4,4 - 4,8 - 4,16
+        // 5,2 - 5,3 - 5,4 - 5,8 - 5,16
+        // 6,2 - 6,3 - 6,4 - 6,8 - 6,16
+        // 7,2 - 7,3 - 7,4 - 7,8 - 7,16
+        // 8,2 - 8,3 - 8,4 - 8,8 - 8,16
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+        // Load values from RHS matrix
+        LOAD_BLOCK(K0, N0, DATA_TYPE, b, rhs_ptr, rhs_offset, rhs_stride_y, zero);
+
+        RHS_VFMA_M0xN0(0, a, b0, c);
+        RHS_VFMA_M0xN0(1, a, b1, c);
+#if K0 > 2
+        RHS_VFMA_M0xN0(2, a, b2, c);
+#endif // K0 > 2
+#if K0 > 3
+        RHS_VFMA_M0xN0(3, a, b3, c);
+#endif // K0 > 3
+#if K0 > 4
+        RHS_VFMA_M0xN0(4, a, b4, c);
+        RHS_VFMA_M0xN0(5, a, b5, c);
+        RHS_VFMA_M0xN0(6, a, b6, c);
+        RHS_VFMA_M0xN0(7, a, b7, c);
+#endif // K0 > 4
+#if K0 > 8
+        RHS_VFMA_M0xN0(8, a, b8, c);
+        RHS_VFMA_M0xN0(9, a, b9, c);
+        RHS_VFMA_M0xN0(A, a, bA, c);
+        RHS_VFMA_M0xN0(B, a, bB, c);
+        RHS_VFMA_M0xN0(C, a, bC, c);
+        RHS_VFMA_M0xN0(D, a, bD, c);
+        RHS_VFMA_M0xN0(E, a, bE, c);
+        RHS_VFMA_M0xN0(F, a, bF, c);
+#endif // K0 > 8
+
+        lhs_offset += K0 * sizeof(DATA_TYPE);
+        rhs_offset += K0 * rhs_stride_y;
+    }
+#endif // K0 > 1
+    // Left-over accumulations
+    for(; i < K; ++i)
+    {
+        // Load values from LHS matrix
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a0 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 0 * lhs_stride_y + zlhs0));
+#if M0 > 1
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a1 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 1 * lhs_stride_y + zlhs1));
+#endif // M0 > 1
+#if M0 > 2
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a2 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 2 * lhs_stride_y + zlhs2));
+#endif // M0 > 2
+#if M0 > 3
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a3 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 3 * lhs_stride_y + zlhs3));
+#endif // M0 > 3
+#if M0 > 4
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a4 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 4 * lhs_stride_y + zlhs4));
+#endif // M0 > 4
+#if M0 > 5
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a5 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 5 * lhs_stride_y + zlhs5));
+#endif // M0 > 5
+#if M0 > 6
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a6 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 6 * lhs_stride_y + zlhs6));
+#endif // M0 > 6
+#if M0 > 7
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zlhs7));
+#endif // M0 > 7
+
+        VEC_DATA_TYPE(DATA_TYPE, N0)
+        b = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 0 * rhs_stride_y));
+        RHS_VFMA_M0xN0(0, a, b, c);
+
+        lhs_offset += sizeof(DATA_TYPE);
+        rhs_offset += rhs_stride_y;
+    }
+
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * dst_stride_y);
+
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0);
+
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
+    const bool cond_y = ((y + 1) * M0 >= M);
+    const bool cond_x = ((x + 1) * N0 >= N);
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zout, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE));
+
+    LOAD_BLOCK_BOUNDARY_AWARE(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, 1, PARTIAL_STORE_N0, false, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(1, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    ADD_BLOCK_BROADCAST(M0, c, bias0);
+
+#else // defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE)) + (get_global_id(1) * (uint)M0 * bias_stride_y) + get_global_id(
+                                    2) * bias_stride_z;
+
+    LOAD_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(M0, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    ADD_BLOCK(M0, c, bias);
+
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    // c = act(c)
+    POST_OP1_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+    // c = c + eltwise_operand (mix-precision, broadcast, boundary aware)
+    POST_OP2_ELTWISE_OP(P2_ELTWISE_OP, M0, N0, c, eltwise_operand, DATA_TYPE, DATA_TYPE_ACCUMULATOR, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+    // c = act(c)
+    POST_OP3_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+
+    // Store output block
+    STORE_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+}
+#endif // defined(P2_ELTWISE_OP) && defined(P2_ELTWISE_ARG1_HEIGHT) && defined(P2_ELTWISE_ARG1_WIDTH)
+#endif // defined(M0) && defined(N0) && defined(K0) && defined(K) && defined(DATA_TYPE) && defined(PARTIAL_STORE_M0) && defined(PARTIAL_STORE_N0)
diff --git a/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl
index 9404c5e..758fd32 100644
--- a/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl
+++ b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl
@@ -352,6 +352,7 @@
 
     REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0);
 
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
     const bool cond_y = ((get_global_id(1) + 1) * M0 >= M);
     const bool cond_x = ((get_global_id(0) + 1) * N0 >= N);
 
@@ -568,6 +569,7 @@
 
     REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0);
 
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
     const bool cond_y = ((get_global_id(1) + 1) * M0 >= M);
     const bool cond_x = ((get_global_id(0) + 1) * N0 >= N);
 
@@ -824,6 +826,7 @@
     const uint y = get_global_id(1);
     const uint z = get_global_id(2);
 
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
     const bool cond_y = ((get_global_id(1) + 1) * M0 >= M);
     const bool cond_x = ((get_global_id(0) + 1) * N0 >= N);
 
@@ -1326,6 +1329,7 @@
 
     REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0);
 
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
     const bool cond_y = ((get_global_id(1) + 1) * M0 >= M);
     const bool cond_x = ((get_global_id(0) + 1) * N0 >= N);
 
diff --git a/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
new file mode 100644
index 0000000..508ee96
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
@@ -0,0 +1,1375 @@
+/*
+ * Copyright (c) 2021 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 "fp_post_ops_act_eltwise_op_act.h"
+#include "gemm_helpers.h"
+#include "repeat.h"
+
+/** (EXPERIMENTAL_POST_OPS) gemm_mm_reshaped_only_rhs kernel */
+#if defined(M0) && defined(N0) && defined(K0) && defined(H0) && defined(DATA_TYPE) && defined(M) && defined(N) && defined(K)
+#if defined(P2_ELTWISE_OP) && defined(P2_ELTWISE_ARG1_HEIGHT) && defined(P2_ELTWISE_ARG1_WIDTH)
+
+#define CONCAT(a, b) a##b
+
+#define ARM_DOT1(a, b, c) \
+    ({                    \
+        c = fma(a, b, c); \
+    })
+#define ARM_DOT2(a, b, c)       \
+    ({                          \
+        c = fma(a.s0, b.s0, c); \
+        c = fma(a.s1, b.s1, c); \
+    })
+#define ARM_DOT3(a, b, c)           \
+    ({                              \
+        ARM_DOT2(a, b, c);          \
+        c = fma((a.s2), (b.s2), c); \
+    })
+#define ARM_DOT4(a, b, c)           \
+    ({                              \
+        ARM_DOT3(a, b, c);          \
+        c = fma((a.s3), (b.s3), c); \
+    })
+#define ARM_DOT8(a, b, c)            \
+    ({                               \
+        ARM_DOT4((a.lo), (b.lo), c); \
+        ARM_DOT4((a.hi), (b.hi), c); \
+    })
+#define ARM_DOT16(a, b, c)           \
+    ({                               \
+        ARM_DOT8((a.lo), (b.lo), c); \
+        ARM_DOT8((a.hi), (b.hi), c); \
+    })
+
+#if N0 == 2
+#define ARM_DOT_K0XN0(k0, a, b, c) \
+    ({                             \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##0), (c.s0));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##1), (c.s1));     \
+    })
+#elif N0 == 3 // N0 == 3
+#define ARM_DOT_K0XN0(k0, a, b, c) \
+    ({                             \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##0), (c.s0));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##1), (c.s1));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##2), (c.s2));     \
+    })
+#elif N0 == 4 // N0 == 4
+#define ARM_DOT_K0XN0(k0, a, b, c) \
+    ({                             \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##0), (c.s0));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##1), (c.s1));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##2), (c.s2));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##3), (c.s3));     \
+    })
+#elif N0 == 8 // N0 == 8
+#define ARM_DOT_K0XN0(k0, a, b, c) \
+    ({                             \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##0), (c.s0));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##1), (c.s1));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##2), (c.s2));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##3), (c.s3));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##4), (c.s4));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##5), (c.s5));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##6), (c.s6));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##7), (c.s7));     \
+    })
+#elif N0 == 16 // N0 == 16
+#define ARM_DOT_K0XN0(k0, a, b, c) \
+    ({                             \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##0), (c.s0));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##1), (c.s1));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##2), (c.s2));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##3), (c.s3));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##4), (c.s4));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##5), (c.s5));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##6), (c.s6));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##7), (c.s7));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##8), (c.s8));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##9), (c.s9));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##A), (c.sA));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##B), (c.sB));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##C), (c.sC));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##D), (c.sD));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##E), (c.sE));     \
+        CONCAT(ARM_DOT, k0)        \
+        ((a), (b##F), (c.sF));     \
+    })
+#else // N0 not supported
+#error "N0 value not supported"
+#endif // N0 conditions
+
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices plus 3 post ops:
+ * Post op 1: activation (optional)
+ * Post op 2: elementwise op
+ * Post op 3: activation (optional)
+ *
+ * @note (Optional) -DP1_ACTIVATION_TYPE, -DP1_ACTIVATION_A_VAL, -DP1_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ * @note (Required) -DP2_ELTWISE_OP: The (binary) elementwise post op to perform
+ * @note (Required) -DP2_ELTWISE_ARG1_HEIGHT: The height (Y dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Required) -DP2_ELTWISE_ARG1_WIDTH: The width (X dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Optional) -DP3_ACTIVATION_TYPE, -DP3_ACTIVATION_A_VAL, -DP3_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ *
+ * All parameters are similarly defined in kernel gemm_mm_reshaped_only_rhs_t, with these additions:
+ *
+ * @param[in] eltwise_operand_ptr      Pointer to the eltwise operand matrix. Supported data type: F16/F32
+ * @param[in] eltwise_operand_stride_x Stride of the eltwise operand matrix in X dimension (in bytes)
+ * @param[in] eltwise_operand_step_x   eltwise_operand_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_y Stride of the eltwise operand matrix in Y dimension (in bytes)
+ * @param[in] eltwise_operand_step_y   eltwise_operand_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_z Stride of the eltwise operand tensor in Z dimension (in bytes)
+ */
+__kernel void gemm_mm_reshaped_only_rhs_t_post_act_eltwise_op_act(IMAGE_DECLARATION(lhs),
+                                                                  IMAGE_DECLARATION(rhs),
+#if defined(BETA)
+                                                                  IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
+                                                                  IMAGE_DECLARATION(dst),
+                                                                  // Post-Op arguments
+                                                                  IMAGE_DECLARATION(eltwise_operand),
+                                                                  uint lhs_stride_z,
+                                                                  uint rhs_stride_z,
+#if defined(BETA)
+                                                                  uint bias_stride_z,
+#endif //defined(BETA)
+                                                                  uint dst_stride_z,
+                                                                  uint eltwise_operand_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                                                  ,
+                                                                  uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                                  ,
+                                                                  uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                                 )
+{
+    // Block size
+#define RHS_BLOCK_SIZE ((K0) * (N0))
+
+    // RHS offset and step X
+#if defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (K0)
+#define RHS_STEP_X ((K0) * (H0))
+#define RHS_STEP_LOOP (1)
+#else // defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+#define RHS_STEP_X (K0)
+#define RHS_STEP_LOOP (H0)
+#endif // defined(RHS_INTERLEAVE)
+
+    uint x = get_global_id(0);
+    uint y = get_global_id(1);
+    uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+    if((x * N0 >= N) || (y * M0 >= M))
+    {
+        return;
+    }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+    // Compute LHS matrix address
+    uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+    // Compute RHS reshaped matrix address
+    uint rhs_offset = rhs_offset_first_element_in_bytes + (x % H0) * (uint)RHS_OFFSET_X * sizeof(DATA_TYPE) + (x / (uint)H0) * rhs_stride_y;
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    rhs_offset += z * rhs_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); //uint zlhs0=0,zlhs1=0,zlhs2=0,... zlhs7=0;
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zlhs, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply lhs_stride_z by DEPTH_GEMM3D
+    lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Initialize the accumulators
+    REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0)    c0=0,c1=0,c2=0,... c(M0-1)=0;
+
+    int i = 0;
+    for(; i <= (K - K0); i += K0)
+    {
+        // Supported cases (M0, K0):
+        // 1,2 - 1,3 - 1,4 - 1,8 - 1,16
+        // 2,2 - 2,3 - 2,4 - 2,8 - 2,16
+        // 3,2 - 3,3 - 3,4 - 3,8 - 3,16
+        // 4,2 - 4,3 - 4,4 - 4,8 - 4,16
+        // 5,2 - 5,3 - 5,4 - 5,8 - 5,16
+        // 6,2 - 6,3 - 6,4 - 6,8 - 6,16
+        // 7,2 - 7,3 - 7,4 - 7,8 - 7,16
+        // 8,2 - 8,3 - 8,4 - 8,8 - 8,16
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+        // Load values from RHS reshaped matrix
+        LOAD_BLOCK(N0, K0, DATA_TYPE, b, rhs_ptr, rhs_offset, RHS_STEP_X * sizeof(DATA_TYPE), zero);
+
+        // Accumulate
+        ARM_DOT_K0XN0(K0, a0, b, c0);
+#if M0 > 1
+        ARM_DOT_K0XN0(K0, a1, b, c1);
+#endif // M0 > 1
+#if M0 > 2
+        ARM_DOT_K0XN0(K0, a2, b, c2);
+#endif // M0 > 2
+#if M0 > 3
+        ARM_DOT_K0XN0(K0, a3, b, c3);
+#endif // M0 > 3
+#if M0 > 4
+        ARM_DOT_K0XN0(K0, a4, b, c4);
+#endif // M0 > 4
+#if M0 > 5
+        ARM_DOT_K0XN0(K0, a5, b, c5);
+#endif // M0 > 5
+#if M0 > 6
+        ARM_DOT_K0XN0(K0, a6, b, c6);
+#endif // M0 > 6
+#if M0 > 7
+        ARM_DOT_K0XN0(K0, a7, b, c7);
+#endif // M0 > 7
+
+        lhs_offset += K0 * sizeof(DATA_TYPE);
+        rhs_offset += (N0 * RHS_STEP_X * RHS_STEP_LOOP) * sizeof(DATA_TYPE);
+    }
+
+    // Left-over accumulations
+    for(; i < K; ++i)
+    {
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, 1, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+        // Load values from RHS reshaped matrix
+        LOAD_BLOCK(N0, 1, DATA_TYPE, b, rhs_ptr, rhs_offset, RHS_STEP_X * sizeof(DATA_TYPE), zero);
+
+        // Accumulate
+        ARM_DOT_K0XN0(1, a0, b, c0);
+#if M0 > 1
+        ARM_DOT_K0XN0(1, a1, b, c1);
+#endif // M0 > 1
+#if M0 > 2
+        ARM_DOT_K0XN0(1, a2, b, c2);
+#endif // M0 > 2
+#if M0 > 3
+        ARM_DOT_K0XN0(1, a3, b, c3);
+#endif // M0 > 3
+#if M0 > 4
+        ARM_DOT_K0XN0(1, a4, b, c4);
+#endif // M0 > 4
+#if M0 > 5
+        ARM_DOT_K0XN0(1, a5, b, c5);
+#endif // M0 > 5
+#if M0 > 6
+        ARM_DOT_K0XN0(1, a6, b, c6);
+#endif // M0 > 6
+#if M0 > 7
+        ARM_DOT_K0XN0(1, a7, b, c7);
+#endif // M0 > 7
+
+        lhs_offset += sizeof(DATA_TYPE);
+        rhs_offset += sizeof(DATA_TYPE);
+    }
+
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * dst_stride_y);
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
+    const bool cond_y = ((y + 1) * M0 >= M);
+    const bool cond_x = ((x + 1) * N0 >= N);
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zout, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE));
+
+    LOAD_BLOCK_BOUNDARY_AWARE(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, 1, PARTIAL_STORE_N0, false, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(1, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    ADD_BLOCK_BROADCAST(M0, c, bias0);
+
+#else // defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * bias_stride_y) + z * bias_stride_z;
+
+    LOAD_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(M0, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    ADD_BLOCK(M0, c, bias);
+
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    // c = act(c)
+    POST_OP1_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+    // c = c + eltwise_operand (mix-precision, broadcast, boundary aware)
+    POST_OP2_ELTWISE_OP(P2_ELTWISE_OP, M0, N0, c, eltwise_operand, DATA_TYPE, DATA_TYPE_ACCUMULATOR, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+    // c = act(c)
+    POST_OP3_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+
+    // Store output block
+    STORE_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+
+#if defined(OPENCL_IMAGE_SUPPORT)
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices plus 3 post ops. The RHS matrix is stored in OpenCL image object.
+ * Post op 1: activation (optional)
+ * Post op 2: elementwise op
+ * Post op 3: activation (optional)
+ *
+ * @note (Optional) -DP1_ACTIVATION_TYPE, -DP1_ACTIVATION_A_VAL, -DP1_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ * @note (Required) -DP2_ELTWISE_OP: The (binary) elementwise post op to perform
+ * @note (Required) -DP2_ELTWISE_ARG1_HEIGHT: The height (Y dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Required) -DP2_ELTWISE_ARG1_WIDTH: The width (X dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Optional) -DP3_ACTIVATION_TYPE, -DP3_ACTIVATION_A_VAL, -DP3_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ *
+ * All parameters are similarly defined in kernel gemm_mm_reshaped_only_rhs_t_texture, with these additions:
+ *
+ * @param[in] eltwise_operand_ptr      Pointer to the eltwise operand matrix. Supported data type: F16/F32
+ * @param[in] eltwise_operand_stride_x Stride of the eltwise operand matrix in X dimension (in bytes)
+ * @param[in] eltwise_operand_step_x   eltwise_operand_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_y Stride of the eltwise operand matrix in Y dimension (in bytes)
+ * @param[in] eltwise_operand_step_y   eltwise_operand_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_z Stride of the eltwise operand tensor in Z dimension (in bytes)
+ */
+__kernel void gemm_mm_reshaped_only_rhs_t_texture_post_act_eltwise_op_act(IMAGE_DECLARATION(lhs),
+                                                                          __read_only image2d_t rhs_img,
+#if defined(BETA)
+                                                                          IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
+                                                                          IMAGE_DECLARATION(dst),
+                                                                          // Post-Op arguments
+                                                                          IMAGE_DECLARATION(eltwise_operand),
+                                                                          uint lhs_stride_z,
+                                                                          uint rhs_stride_z,
+#if defined(BETA)
+                                                                          uint bias_stride_z,
+#endif //defined(BETA)
+                                                                          uint dst_stride_z,
+                                                                          uint eltwise_operand_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                                                          ,
+                                                                          uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                                          ,
+                                                                          uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                                         )
+{
+    // Pixel unit
+#define PIXEL_UNIT CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(K0)
+
+#define LEFTOVER_K (K % K0)
+
+    // Block size
+#define RHS_BLOCK_SIZE (PIXEL_UNIT * (N0))
+
+    // RHS offset and step X
+#if defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (PIXEL_UNIT)
+#define RHS_STEP_X (PIXEL_UNIT * (H0))
+#define RHS_STEP_LOOP (1)
+#else // defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+#define RHS_STEP_X PIXEL_UNIT
+#define RHS_STEP_LOOP (H0)
+#endif // defined(RHS_INTERLEAVE)
+
+    uint x = get_global_id(0);
+    uint y = get_global_id(1);
+    uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+    if((x * N0 >= N) || (y * M0 >= M))
+    {
+        return;
+    }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+    // Compute LHS matrix address
+    uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    const uint z_rhs = (get_global_id(2) % MATRIX_B_DEPTH);
+#else  // defined(MATRIX_B_DEPTH)
+    const uint z_rhs = get_global_id(2);
+#endif // defined(MATRIX_B_DEPTH)
+
+    // Compute RHS matrix coordinates
+    uint       x_rhs = (get_global_id(0) % H0) * (uint)RHS_OFFSET_X;
+    const uint y_rhs = (get_global_id(0) / (uint)H0) + z_rhs * RHS_HEIGHT;
+
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zlhs, 0);
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zlhs, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply lhs_stride_z by DEPTH_GEMM3D
+    lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Initialize the accumulators
+    REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0);
+
+    int i = 0;
+    for(; i <= (K - K0); i += K0)
+    {
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+        // Load values from RHS matrix stored in a cl_image
+        REPEAT_VAR_INIT_TO_CONST(N0, VEC_DATA_TYPE(DATA_TYPE, K0), b, 0);
+        LOAD_TEXTURE2D(N0, PIXEL_UNIT, DATA_TYPE, b, rhs_img, x_rhs, y_rhs, RHS_STEP_X, 0);
+
+        // Accumulate
+        ARM_DOT_K0XN0(K0, a0, b, c0);
+#if M0 > 1
+        ARM_DOT_K0XN0(K0, a1, b, c1);
+#endif // M0 > 1
+#if M0 > 2
+        ARM_DOT_K0XN0(K0, a2, b, c2);
+#endif // M0 > 2
+#if M0 > 3
+        ARM_DOT_K0XN0(K0, a3, b, c3);
+#endif // M0 > 3
+#if M0 > 4
+        ARM_DOT_K0XN0(K0, a4, b, c4);
+#endif // M0 > 4
+#if M0 > 5
+        ARM_DOT_K0XN0(K0, a5, b, c5);
+#endif // M0 > 5
+#if M0 > 6
+        ARM_DOT_K0XN0(K0, a6, b, c6);
+#endif // M0 > 6
+#if M0 > 7
+        ARM_DOT_K0XN0(K0, a7, b, c7);
+#endif // M0 > 7
+
+        lhs_offset += K0 * sizeof(DATA_TYPE);
+        x_rhs += N0 * RHS_STEP_X * RHS_STEP_LOOP;
+    }
+
+#if LEFTOVER_K != 0
+    // Note: We cannot read out-of-bound elements from the RHS matrix because
+    // the RHS width is always multiple of K0. This is not be true for the LHS matrix
+
+    union UNION_VEC_TYPE
+    {
+        DATA_TYPE s[K0];
+        VEC_DATA_TYPE(DATA_TYPE, K0)
+        v;
+    };
+
+    union UNION_VEC_TYPE a0 = {.v = 0 };
+#if M0 > 1
+    union UNION_VEC_TYPE a1 = {.v = 0 };
+#endif // M0 > 1
+#if M0 > 2
+    union UNION_VEC_TYPE a2 = {.v = 0 };
+#endif // M0 > 2
+#if M0 > 3
+    union UNION_VEC_TYPE a3 = {.v = 0 };
+#endif // M0 > 3
+#if M0 > 4
+    union UNION_VEC_TYPE a4 = {.v = 0 };
+#endif // M0 > 4
+#if M0 > 5
+    union UNION_VEC_TYPE a5 = {.v = 0 };
+#endif // M0 > 5
+#if M0 > 6
+    union UNION_VEC_TYPE a6 = {.v = 0 };
+#endif // M0 > 6
+#if M0 > 7
+    union UNION_VEC_TYPE a7 = {.v = 0 };
+#endif // M0 > 7
+
+    REPEAT_VAR_INIT_TO_CONST(N0, VEC_DATA_TYPE(DATA_TYPE, K0), b, 0);
+
+    // Load from RHS matrix
+    LOAD_TEXTURE2D(N0, PIXEL_UNIT, DATA_TYPE, b, rhs_img, x_rhs, y_rhs, RHS_STEP_X, 0);
+
+    // Load from LHS matrix
+    for(int k = 0; k < LEFTOVER_K; ++k)
+    {
+        a0.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 0 * lhs_stride_y + zlhs0);
+#if M0 > 1
+        a1.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 1 * lhs_stride_y + zlhs1);
+#endif // M0 > 1
+#if M0 > 2
+        a2.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 2 * lhs_stride_y + zlhs2);
+#endif // M0 > 2
+#if M0 > 3
+        a3.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 3 * lhs_stride_y + zlhs3);
+#endif // M0 > 3
+#if M0 > 4
+        a4.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 4 * lhs_stride_y + zlhs4);
+#endif // M0 > 4
+#if M0 > 5
+        a5.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 5 * lhs_stride_y + zlhs5);
+#endif // M0 > 5
+#if M0 > 6
+        a6.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 6 * lhs_stride_y + zlhs6);
+#endif // M0 > 6
+#if M0 > 7
+        a7.s[k] = *(__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zlhs7);
+#endif // M0 > 7
+
+        lhs_offset += sizeof(DATA_TYPE);
+    }
+
+    // Accumulate
+    ARM_DOT_K0XN0(K0, a0.v, b, c0);
+#if M0 > 1
+    ARM_DOT_K0XN0(K0, a1.v, b, c1);
+#endif // M0 > 1
+#if M0 > 2
+    ARM_DOT_K0XN0(K0, a2.v, b, c2);
+#endif // M0 > 2
+#if M0 > 3
+    ARM_DOT_K0XN0(K0, a3.v, b, c3);
+#endif // M0 > 3
+#if M0 > 4
+    ARM_DOT_K0XN0(K0, a4.v, b, c4);
+#endif // M0 > 4
+#if M0 > 5
+    ARM_DOT_K0XN0(K0, a5.v, b, c5);
+#endif // M0 > 5
+#if M0 > 6
+    ARM_DOT_K0XN0(K0, a6.v, b, c6);
+#endif // M0 > 6
+#if M0 > 7
+    ARM_DOT_K0XN0(K0, a7.v, b, c7);
+#endif // M0 > 7
+
+#endif // LEFTOVER_K != 0
+
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * dst_stride_y);
+
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
+    const bool cond_y = ((y + 1) * M0 >= M);
+    const bool cond_x = ((x + 1) * N0 >= N);
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zout, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE));
+
+    LOAD_BLOCK_BOUNDARY_AWARE(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, 1, PARTIAL_STORE_N0, false, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(1, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    ADD_BLOCK_BROADCAST(M0, c, bias0);
+
+#else // defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * bias_stride_y) + z * bias_stride_z;
+
+    LOAD_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(M0, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    ADD_BLOCK(M0, c, bias);
+
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    // c = act(c)
+    POST_OP1_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+    // c = c + eltwise_operand (mix-precision, broadcast, boundary aware)
+    POST_OP2_ELTWISE_OP(P2_ELTWISE_OP, M0, N0, c, eltwise_operand, DATA_TYPE, DATA_TYPE_ACCUMULATOR, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+    // c = act(c)
+    POST_OP3_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+
+    // Store output block
+    STORE_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+#undef LEFTOVER_K
+#undef PIXEL_UNIT
+}
+#endif // defined(OPENCL_IMAGE_SUPPORT)
+
+#define VFMA(a, b, c)     \
+    ({                    \
+        c = fma(a, b, c); \
+    })
+
+#if M0 == 1
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+    })
+#elif M0 == 2 // M0 == 2
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+    })
+#elif M0 == 3 // M0 == 3
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+    })
+#elif M0 == 4 // M0 == 4
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+    })
+#elif M0 == 5 // M0 == 5
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+    })
+#elif M0 == 6 // M0 == 6
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+    })
+#elif M0 == 7 // M0 == 7
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+    })
+#elif M0 == 8 // M0 == 8
+#define VFMA_M0xN0(i, a, b, c)                                        \
+    ({                                                                \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+        VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##7).s##i), b, (c##7)); \
+    })
+#else // M0 not supported
+#error "M0 not supported"
+#endif // M0 not supported
+
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices plus 3 post ops:
+ * Post op 1: activation (optional)
+ * Post op 2: elementwise op
+ * Post op 3: activation (optional)
+ *
+ * @note (Optional) -DP1_ACTIVATION_TYPE, -DP1_ACTIVATION_A_VAL, -DP1_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ * @note (Required) -DP2_ELTWISE_OP: The (binary) elementwise post op to perform
+ * @note (Required) -DP2_ELTWISE_ARG1_HEIGHT: The height (Y dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Required) -DP2_ELTWISE_ARG1_WIDTH: The width (X dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Optional) -DP3_ACTIVATION_TYPE, -DP3_ACTIVATION_A_VAL, -DP3_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ *
+ * All parameters are similarly defined in kernel gemm_mm_reshaped_only_rhs_nt, with these additions:
+ *
+ * @param[in] eltwise_operand_ptr      Pointer to the eltwise operand matrix. Supported data type: F16/F32
+ * @param[in] eltwise_operand_stride_x Stride of the eltwise operand matrix in X dimension (in bytes)
+ * @param[in] eltwise_operand_step_x   eltwise_operand_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_y Stride of the eltwise operand matrix in Y dimension (in bytes)
+ * @param[in] eltwise_operand_step_y   eltwise_operand_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_z Stride of the eltwise operand tensor in Z dimension (in bytes)
+ */
+__kernel void gemm_mm_reshaped_only_rhs_nt_post_act_eltwise_op_act(IMAGE_DECLARATION(lhs),
+                                                                   IMAGE_DECLARATION(rhs),
+#if defined(BETA)
+                                                                   IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
+                                                                   IMAGE_DECLARATION(dst),
+                                                                   // Post-Op arguments
+                                                                   IMAGE_DECLARATION(eltwise_operand),
+                                                                   uint lhs_stride_z,
+                                                                   uint rhs_stride_z,
+#if defined(BETA)
+                                                                   uint bias_stride_z,
+#endif //defined(BETA)
+                                                                   uint dst_stride_z,
+                                                                   uint eltwise_operand_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                                                   ,
+                                                                   uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                                   ,
+                                                                   uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                                  )
+{
+    // Block size
+#define RHS_BLOCK_SIZE ((K0) * (N0))
+
+    // RHS offset and step X
+#if defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (N0)
+#define RHS_STEP_X ((N0) * (H0))
+#define RHS_STEP_LOOP (1)
+#else // defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+#define RHS_STEP_X (N0)
+#define RHS_STEP_LOOP (H0)
+#endif // defined(RHS_INTERLEAVE)
+
+    uint x = get_global_id(0);
+    uint y = get_global_id(1);
+    uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+    if((x * N0 >= N) || (y * M0 >= M))
+    {
+        return;
+    }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+    // Compute LHS matrix address
+    uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+    // Compute RHS reshaped matrix address
+    uint rhs_offset = rhs_offset_first_element_in_bytes + (x % H0) * (uint)RHS_OFFSET_X * sizeof(DATA_TYPE) + (x / (uint)H0) * rhs_stride_y;
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    rhs_offset += z * rhs_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zin, 0);   //uint zin0=0,zin1=0,zin2=0,... zin7=0;
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0); //uint zero0=0,zero1=0,zero2=0,... zero7=0;
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+
+    // The plane (zin) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zin, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply lhs_stride_z by DEPTH_GEMM3D
+    lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Initialize the accumulators
+    REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0)    c0=0,c1=0,c2=0,... c(N0-1)=0;
+
+    int i = 0;
+    for(; i <= (K - K0); i += K0)
+    {
+        // Supported cases (M0, K0):
+        // 1,2 - 1,3 - 1,4 - 1,8 - 1,16
+        // 2,2 - 2,3 - 2,4 - 2,8 - 2,16
+        // 3,2 - 3,3 - 3,4 - 3,8 - 3,16
+        // 4,2 - 4,3 - 4,4 - 4,8 - 4,16
+        // 5,2 - 5,3 - 5,4 - 5,8 - 5,16
+        // 6,2 - 6,3 - 6,4 - 6,8 - 6,16
+        // 7,2 - 7,3 - 7,4 - 7,8 - 7,16
+        // 8,2 - 8,3 - 8,4 - 8,8 - 8,16
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zin);
+
+        VEC_DATA_TYPE(DATA_TYPE, N0)
+        b0;
+
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 0 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(0, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 1 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(1, a, b0, c);
+#if K0 > 2
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 2 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(2, a, b0, c);
+#endif // K0 > 2
+#if K0 > 3
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 3 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(3, a, b0, c);
+#endif // K0 > 3
+#if K0 > 4
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 4 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(4, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 5 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(5, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 6 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(6, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 7 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(7, a, b0, c);
+#endif // K0 > 4
+#if K0 > 8
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 8 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(8, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 9 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(9, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 10 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(A, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 11 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(B, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 12 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(C, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 13 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(D, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 14 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(E, a, b0, c);
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 15 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(F, a, b0, c);
+#endif // K0 > 8
+
+        lhs_offset += K0 * sizeof(DATA_TYPE);
+        rhs_offset += K0 * RHS_STEP_X * RHS_STEP_LOOP * sizeof(DATA_TYPE);
+    }
+
+    // Left-over accumulations
+    for(; i < K; ++i)
+    {
+        // Load values from LHS matrix
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a0 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 0 * lhs_stride_y + zin0));
+#if M0 > 1
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a1 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 1 * lhs_stride_y + zin1));
+#endif // M0 > 1
+#if M0 > 2
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a2 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 2 * lhs_stride_y + zin2));
+#endif // M0 > 2
+#if M0 > 3
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a3 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 3 * lhs_stride_y + zin3));
+#endif // M0 > 3
+#if M0 > 4
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a4 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 4 * lhs_stride_y + zin4));
+#endif // M0 > 4
+#if M0 > 5
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a5 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 5 * lhs_stride_y + zin5));
+#endif // M0 > 5
+#if M0 > 6
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a6 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 6 * lhs_stride_y + zin6));
+#endif // M0 > 6
+#if M0 > 7
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zin7));
+#endif // M0 > 7
+
+        VEC_DATA_TYPE(DATA_TYPE, N0)
+        b0;
+
+        b0 = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 0 * RHS_STEP_X * sizeof(DATA_TYPE)));
+        VFMA_M0xN0(0, a, b0, c);
+
+        lhs_offset += sizeof(DATA_TYPE);
+        rhs_offset += RHS_STEP_X * sizeof(DATA_TYPE);
+    }
+
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * dst_stride_y);
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
+    const bool cond_y = ((y + 1) * M0 >= M);
+    const bool cond_x = ((x + 1) * N0 >= N);
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zout, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE));
+
+    LOAD_BLOCK_BOUNDARY_AWARE(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, 1, PARTIAL_STORE_N0, false, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(1, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    ADD_BLOCK_BROADCAST(M0, c, bias0);
+
+#else // defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * bias_stride_y) + z * bias_stride_z;
+
+    LOAD_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(M0, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    ADD_BLOCK(M0, c, bias);
+
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    // c = act(c)
+    POST_OP1_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+    // c = c + eltwise_operand (mix-precision, broadcast, boundary aware)
+    POST_OP2_ELTWISE_OP(P2_ELTWISE_OP, M0, N0, c, eltwise_operand, DATA_TYPE, DATA_TYPE_ACCUMULATOR, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+    // c = act(c)
+    POST_OP3_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+
+    // Store output block
+    STORE_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+
+#if defined(OPENCL_IMAGE_SUPPORT)
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices plus 3 post ops. The RHS matrix is stored in OpenCL image object.
+ * Post op 1: activation (optional)
+ * Post op 2: elementwise op
+ * Post op 3: activation (optional)
+ *
+ * @note (Optional) -DP1_ACTIVATION_TYPE, -DP1_ACTIVATION_A_VAL, -DP1_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ * @note (Required) -DP2_ELTWISE_OP: The (binary) elementwise post op to perform
+ * @note (Required) -DP2_ELTWISE_ARG1_HEIGHT: The height (Y dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Required) -DP2_ELTWISE_ARG1_WIDTH: The width (X dimension) of the eltwise operand matrix of the eltwise post op at slot 2
+ * @note (Optional) -DP3_ACTIVATION_TYPE, -DP3_ACTIVATION_A_VAL, -DP3_ACTIVATION_B_VAL: The activation type, alpha and beta values of the activation post op at slot 3
+ *
+ * All parameters are similarly defined in kernel gemm_mm_reshaped_only_rhs_nt_texture, with these additions:
+ *
+ * @param[in] eltwise_operand_ptr      Pointer to the eltwise operand matrix. Supported data type: F16/F32
+ * @param[in] eltwise_operand_stride_x Stride of the eltwise operand matrix in X dimension (in bytes)
+ * @param[in] eltwise_operand_step_x   eltwise_operand_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_y Stride of the eltwise operand matrix in Y dimension (in bytes)
+ * @param[in] eltwise_operand_step_y   eltwise_operand_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] eltwise_operand_stride_z Stride of the eltwise operand tensor in Z dimension (in bytes)
+ */
+__kernel void gemm_mm_reshaped_only_rhs_nt_texture_post_act_eltwise_op_act(IMAGE_DECLARATION(lhs),
+                                                                           __read_only image2d_t rhs_img,
+#if defined(BETA)
+                                                                           IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
+                                                                           IMAGE_DECLARATION(dst),
+                                                                           // Post-Op arguments
+                                                                           IMAGE_DECLARATION(eltwise_operand),
+                                                                           uint lhs_stride_z,
+                                                                           uint rhs_stride_z,
+#if defined(BETA)
+                                                                           uint bias_stride_z,
+#endif //defined(BETA)
+                                                                           uint dst_stride_z,
+                                                                           uint eltwise_operand_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                                                           ,
+                                                                           uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                                           ,
+                                                                           uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                                          )
+{
+    // Pixel unit
+#define PIXEL_UNIT CONVERT_VECTOR_SIZE_TO_PIXEL_UNIT(N0)
+
+    // Block size
+#define RHS_BLOCK_SIZE ((K0) * (PIXEL_UNIT))
+
+    // RHS offset and step X
+#if defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (PIXEL_UNIT)
+#define RHS_STEP_X ((PIXEL_UNIT) * (H0))
+#else // defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+#define RHS_STEP_X (PIXEL_UNIT)
+#endif // defined(RHS_INTERLEAVE)
+
+    uint x = get_global_id(0);
+    uint y = get_global_id(1);
+    uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+    if((x * N0 >= N) || (y * M0 >= M))
+    {
+        return;
+    }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+    // Compute LHS matrix address
+    uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    const uint z_rhs = (z % MATRIX_B_DEPTH);
+#else  // defined(MATRIX_B_DEPTH)
+    const uint z_rhs = z;
+#endif // defined(MATRIX_B_DEPTH)
+
+    // Compute RHS matrix coordinates
+    uint       x_rhs = (x % H0) * (uint)RHS_OFFSET_X;
+    const uint y_rhs = (x / (uint)H0) + z_rhs * RHS_HEIGHT;
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zin, 0);
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+
+    // The plane (zin) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zin, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply lhs_stride_z by DEPTH_GEMM3D
+    lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Initialize the accumulators
+    REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0);
+
+    int i = 0;
+    for(; i <= (K - K0); i += K0)
+    {
+        // Load values from LHS matrix
+        LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zin);
+
+        VEC_DATA_TYPE(DATA_TYPE, N0)
+        b0;
+
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 0 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(0, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 1 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(1, a, b0, c);
+#if K0 > 2
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 2 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(2, a, b0, c);
+#endif // K0 > 2
+#if K0 > 3
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 3 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(3, a, b0, c);
+#endif // K0 > 3
+#if K0 > 4
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 4 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(4, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 5 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(5, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 6 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(6, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 7 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(7, a, b0, c);
+#endif // K0 > 4
+#if K0 > 8
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 8 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(8, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 9 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(9, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 10 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(A, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 11 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(B, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 12 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(C, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 13 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(D, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 14 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(E, a, b0, c);
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 15 * RHS_STEP_X), (y_rhs));
+        VFMA_M0xN0(F, a, b0, c);
+#endif // K0 > 8
+
+        lhs_offset += K0 * sizeof(DATA_TYPE);
+        x_rhs += K0 * RHS_STEP_X * RHS_STEP_LOOP;
+    }
+
+    // Left-over accumulations
+    for(; i < K; ++i)
+    {
+        // Load values from LHS matrix
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a0 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 0 * lhs_stride_y + zin0));
+#if M0 > 1
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a1 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 1 * lhs_stride_y + zin1));
+#endif // M0 > 1
+#if M0 > 2
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a2 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 2 * lhs_stride_y + zin2));
+#endif // M0 > 2
+#if M0 > 3
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a3 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 3 * lhs_stride_y + zin3));
+#endif // M0 > 3
+#if M0 > 4
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a4 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 4 * lhs_stride_y + zin4));
+#endif // M0 > 4
+#if M0 > 5
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a5 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 5 * lhs_stride_y + zin5));
+#endif // M0 > 5
+#if M0 > 6
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a6 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 6 * lhs_stride_y + zin6));
+#endif // M0 > 6
+#if M0 > 7
+        VEC_DATA_TYPE(DATA_TYPE, 2)
+        a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zin7));
+#endif // M0 > 7
+
+        VEC_DATA_TYPE(DATA_TYPE, N0)
+        b0;
+        b0 = READ_IMAGE2D(DATA_TYPE, PIXEL_UNIT, rhs_img, (x_rhs + 0 * RHS_STEP_X), (y_rhs));
+
+        VFMA_M0xN0(0, a, b0, c);
+
+        lhs_offset += sizeof(DATA_TYPE);
+        x_rhs += RHS_STEP_X;
+    }
+
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * dst_stride_y);
+
+    REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+    // Boundary conditions: detect if current block is at the "bottom" or "right" boundary
+    const bool cond_y = ((y + 1) * M0 >= M);
+    const bool cond_x = ((x + 1) * N0 >= N);
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+    CALCULATE_Z_OFFSET(M0, uint, zout, y * M0, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+    // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+    SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+    // Add beta*bias
+#if defined(BETA)
+#if defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE));
+
+    LOAD_BLOCK_BOUNDARY_AWARE(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, 1, PARTIAL_STORE_N0, false, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(1, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias[broadcasted]
+    ADD_BLOCK_BROADCAST(M0, c, bias0);
+
+#else // defined(BROADCAST_BIAS)
+    __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * M0 * bias_stride_y) + z * bias_stride_z;
+
+    LOAD_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#ifndef UNIT_BETA
+    SCALE_BLOCK(M0, DATA_TYPE, bias, BETA);
+#endif // UNIT_BIAS
+
+    // c = c + bias
+    ADD_BLOCK(M0, c, bias);
+
+#endif // defined(BROADCAST_BIAS)
+#endif // defined(BETA)
+
+    // c = act(c)
+    POST_OP1_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+    // c = c + eltwise_operand (mix-precision, broadcast, boundary aware)
+    POST_OP2_ELTWISE_OP(P2_ELTWISE_OP, M0, N0, c, eltwise_operand, DATA_TYPE, DATA_TYPE_ACCUMULATOR, zero, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+    // c = act(c)
+    POST_OP3_ACTIVATION_OPTIONAL(M0, DATA_TYPE, DATA_TYPE_ACCUMULATOR, N0, c);
+
+    // Store output block
+    STORE_BLOCK_BOUNDARY_AWARE(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout, PARTIAL_STORE_M0, PARTIAL_STORE_N0, cond_y, cond_x);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+#endif // defined(OPENCL_IMAGE_SUPPORT)
+#endif // defined(P2_ELTWISE_OP) && defined(P2_ELTWISE_ARG1_HEIGHT) && defined(P2_ELTWISE_ARG1_WIDTH)
+#endif // defined(M0) && defined(N0) && defined(K0) && defined(H0) && defined(DATA_TYPE) && defined(M) && defined(N) && defined(K)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/common/gemm.cl b/src/core/CL/cl_kernels/common/gemm.cl
index 9732588..6502dd4 100644
--- a/src/core/CL/cl_kernels/common/gemm.cl
+++ b/src/core/CL/cl_kernels/common/gemm.cl
@@ -1000,6 +1000,7 @@
 /** This OpenCL kernel computes the matrix multiplication between 2 matrices.
  *  The LHS matrix is NOT reshaped
  *  The RHS is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is transposed
+ * @note This kernel is duplicated in /experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
  *
  * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
  * @note The GEMM's dimensions (M,N and K) must be passed at compile time using -DM, -DN and and -DK (e.g. -DM=52, -DN=30 and -DK=90)
@@ -1294,6 +1295,7 @@
 /** This OpenCL kernel computes the matrix multiplication between 2 matrices. The RHS matrix is stored in OpenCL image
  *  The LHS matrix is NOT reshaped
  *  The RHS is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is transposed
+ * @note This kernel is duplicated in /experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
  *
  * @note -DOPENCL_IMAGE_SUPPORT must be passed at compile time in order to compile this OpenCL kernel
  * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
@@ -1720,6 +1722,7 @@
 /** This OpenCL kernel computes the matrix multiplication between 2 matrices.
  *  The LHS matrix is NOT reshaped
  *  The RHS is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is NOT transposed
+ * @note This kernel is duplicated in /experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
  *
  * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
  * @note The GEMM's dimensions (M,N and K) must be passed at compile time using -DM, -DN and and -DK (e.g. -DM=52, -DN=30 and -DK=90).
@@ -2038,6 +2041,7 @@
 /** This OpenCL kernel computes the matrix multiplication between 2 matrices.
  *  The LHS matrix is NOT reshaped
  *  The RHS is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is NOT transposed
+ * @note This kernel is duplicated in /experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl
  *
  * @note -DOPENCL_IMAGE_SUPPORT must be passed at compile time in order to compile this OpenCL kernel
  * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
@@ -4025,6 +4029,7 @@
 /** This OpenCL kernel computes the matrix multiplication between 2 matrices.
  *  The LHS matrix is NOT reshaped
  *  The RHS matrix is NOT reshaped
+ * @note This kernel is duplicated in /experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl
  *
  * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
  * @note The GEMM's dimensions (M,N and K) must be passed at compile time using -DM, -DN and and -DK (e.g. -DM=52, -DN=30 and -DK=90)
diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp
index cbc4caf..c47cf8e 100644
--- a/src/gpu/cl/ClKernelLibrary.cpp
+++ b/src/gpu/cl/ClKernelLibrary.cpp
@@ -272,6 +272,7 @@
     { "gemm_mv", "common/gemv.cl" },
     { "gemm_mv_quantized", "common/gemv.cl" },
     { "gemm_mm_native", "common/gemm.cl" },
+    { "gemm_mm_native_post_act_eltwise_op_act", "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl" },
     { "gemm_mm_reshaped_lhs_nt_rhs_t", "common/gemm.cl" },
     { "gemm_mm_reshaped_lhs_nt_rhs_t_texture", "common/gemm.cl" },
     { "gemm_mm_reshaped_lhs_t_rhs_nt", "common/gemm.cl" },
@@ -284,6 +285,10 @@
     { "gemm_mm_reshaped_only_rhs_nt_texture", "common/gemm.cl" },
     { "gemm_mm_reshaped_only_rhs_t", "common/gemm.cl" },
     { "gemm_mm_reshaped_only_rhs_t_texture", "common/gemm.cl" },
+    { "gemm_mm_reshaped_only_rhs_nt_post_act_eltwise_op_act", "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl" },
+    { "gemm_mm_reshaped_only_rhs_nt_texture_post_act_eltwise_op_act", "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl" },
+    { "gemm_mm_reshaped_only_rhs_t_post_act_eltwise_op_act", "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl" },
+    { "gemm_mm_reshaped_only_rhs_t_texture_post_act_eltwise_op_act", "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl" },
     { "gemm_lc_vm_f32", "common/gemm.cl" },
     { "gemm_reshape_lhs_matrix_nt", "common/gemm.cl" },
     { "gemm_reshape_lhs_matrix_t", "common/gemm.cl" },
@@ -585,10 +590,18 @@
 #include "./cl_kernels/common/gemm.clembed"
     },
     {
+        "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl",
+#include "./cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.clembed"
+    },
+    {
         "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl",
 #include "./cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.clembed"
     },
     {
+        "common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl",
+#include "./cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.clembed"
+    },
+    {
         "common/gemmlowp.cl",
 #include "./cl_kernels/common/gemmlowp.clembed"
     },
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
index e389ce5..7ad3d55 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
@@ -33,6 +33,8 @@
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLUtils.h"
+#include "src/core/experimental/PostOp.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
@@ -49,6 +51,17 @@
 {
 using ElementsProcessed = Steps;
 
+const auto post_op_utils = experimental::PostOpCLKernelUtils(
+{
+    //  PostOp sequence                   -> {Kernel Postfix, PostOp Slots}
+    { {}, { "", {} } },
+    { { experimental::PostOpType::Activation }, { "", { 1 } } },
+    { { experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 2 } } },
+    { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 1, 2 } } },
+    { { experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
+    { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } }
+});
+
 Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
                           const GEMMRHSMatrixInfo &rhs_info,
                           const GEMMKernelInfo    &gemm_info)
@@ -68,6 +81,7 @@
                                     "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.is_post_op_sequence_supported(gemm_info.post_ops), "The sequence of Post Ops is not supported");
 
     const unsigned int m = gemm_info.m;
     const unsigned int n = gemm_info.n;
@@ -110,6 +124,7 @@
         const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.are_post_op_shapes_compliant(dst, gemm_info.post_ops), "The Post Op shapes are not compliant");
     }
 
     return Status{};
@@ -170,16 +185,17 @@
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
-
     // dst tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
 
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
+
     auto padding_info         = get_padding_info({ src0, src1, src2, dst });
     _reinterpret_input_as_3d  = gemm_info.reinterpret_input_as_3d;
     _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
     _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
     _add_bias                 = src2 != nullptr;
+    _num_post_op_args         = gemm_info.post_ops.total_num_arguments();
 
     // In case both input and dst have to be reinterpreted as 3D tensors,
     // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
@@ -237,11 +253,20 @@
     build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
     build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
     build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
+    // If post_ops are used, then we disable the use of gemm_info.activation_info
+    if(gemm_info.post_ops.size() > 0)
+    {
+        post_op_utils.set_post_ops_cl_build_options(build_opts, gemm_info.post_ops);
+    }
+    else
+    {
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
+    }
 
     std::string kernel_name("gemm_mm_native");
+    post_op_utils.set_post_ops_cl_kernel_name(kernel_name, gemm_info.post_ops);
 
     // Create kernel
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
@@ -323,11 +348,11 @@
         unsigned int idx0;
         if(_add_bias)
         {
-            idx0 = 4 * num_arguments_per_2D_tensor() + 4;
+            idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + (4 + _num_post_op_args);
         }
         else
         {
-            idx0 = 3 * num_arguments_per_2D_tensor() + 3;
+            idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + (3 + _num_post_op_args);
         }
         const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
@@ -339,11 +364,11 @@
         unsigned int idx0;
         if(_add_bias)
         {
-            idx0 = 4 * num_arguments_per_2D_tensor() + 4 + (_reinterpret_input_as_3d ? 1 : 0);
+            idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + 4 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args;
         }
         else
         {
-            idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
+            idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args;
         }
         const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
@@ -367,6 +392,12 @@
             add_2D_tensor_argument(idx, src2, slice);
         }
         add_2D_tensor_argument(idx, dst, slice);
+        // post op argument buffers
+        for(size_t i = 0; i < _num_post_op_args; ++i)
+        {
+            const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
+            add_2D_tensor_argument(idx, post_op_arg, slice);
+        }
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
         if(_add_bias)
@@ -374,6 +405,12 @@
             _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
         }
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
+        // post op argument stride_z
+        for(size_t i = 0; i < _num_post_op_args; ++i)
+        {
+            const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(post_op_arg->info()->strides_in_bytes()[2]));
+        }
         enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
     }
     while(window.slide_window_slice_3D(slice));
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
index 89837cc..415eb7b 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
@@ -76,11 +76,12 @@
     void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
 
 private:
-    bool _slide_matrix_b{ true };
-    bool _reinterpret_input_as_3d{ false };
-    bool _reinterpret_output_as_3d{ false };
-    bool _use_dummy_work_items{ false };
-    bool _add_bias{ false };
+    bool         _slide_matrix_b{ true };
+    bool         _reinterpret_input_as_3d{ false };
+    bool         _reinterpret_output_as_3d{ false };
+    bool         _use_dummy_work_items{ false };
+    bool         _add_bias{ false };
+    unsigned int _num_post_op_args{ 0 }; // (EXPERIMENTAL_POST_OPS) total number of post op arguments
 };
 } // namespace kernels
 } // namespace opencl
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
index 04c1cd6..260ed13 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
@@ -27,6 +27,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CL/CLUtils.h"
 #include "src/core/CL/CLValidate.h"
+#include "src/core/experimental/PostOp.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
@@ -44,6 +45,17 @@
 {
 using ElementsProcessed = Steps;
 
+const auto post_op_utils = experimental::PostOpCLKernelUtils(
+{
+    //  PostOp sequence                   -> {Kernel Postfix, PostOp Slots}
+    { {}, { "", {} } },
+    { { experimental::PostOpType::Activation }, { "", { 1 } } },
+    { { experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 2 } } },
+    { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 1, 2 } } },
+    { { experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
+    { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } }
+});
+
 Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
                           const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
 {
@@ -64,6 +76,7 @@
                                     "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
     ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info));
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.is_post_op_sequence_supported(gemm_info.post_ops), "The sequence of Post Ops is not supported");
 
     const unsigned int m = gemm_info.m;
     const unsigned int n = gemm_info.n;
@@ -109,6 +122,7 @@
         const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.are_post_op_shapes_compliant(dst, gemm_info.post_ops), "The Post Op shapes are not compliant");
     }
 
     return Status{};
@@ -168,6 +182,9 @@
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
 
+    // dst tensor auto initialization if not yet initialized
+    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
+
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
 
     _reinterpret_input_as_3d  = gemm_info.reinterpret_input_as_3d;
@@ -176,9 +193,7 @@
     _add_bias                 = src2 != nullptr;
     _export_to_cl_image       = rhs_info.export_to_cl_image;
     _has_pad_y                = gemm_info.has_pad_y;
-
-    // dst tensor auto initialization if not yet initialized
-    auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
+    _num_post_op_args         = gemm_info.post_ops.total_num_arguments();
 
     auto padding_info = get_padding_info({ src0, src1, src2, dst });
 
@@ -239,9 +254,6 @@
     build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
     build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
     build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
-    build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
     if(_has_pad_y)
     {
         build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
@@ -249,10 +261,22 @@
         build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d));
         build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
     }
+    // If post_ops are used, then we disable the use of gemm_info.activation_info
+    if(gemm_info.post_ops.size() > 0)
+    {
+        post_op_utils.set_post_ops_cl_build_options(build_opts, gemm_info.post_ops);
+    }
+    else
+    {
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
+        build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
+    }
 
     std::string kernel_name("gemm_mm_reshaped_only_rhs_");
     kernel_name += rhs_info.transpose ? "t" : "nt";
     kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
+    post_op_utils.set_post_ops_cl_kernel_name(kernel_name, gemm_info.post_ops);
 
     // Create kernel
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
@@ -375,6 +399,13 @@
         // dst buffer
         add_2D_tensor_argument(idx, dst, slice);
 
+        // post op argument buffers
+        for(size_t i = 0; i < _num_post_op_args; ++i)
+        {
+            const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
+            add_2D_tensor_argument(idx, post_op_arg, slice);
+        }
+
         // LHS stride_z
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[lhs_idx_batch_size]));
 
@@ -389,6 +420,12 @@
 
         // dst stride_z
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[out_idx_batch_size]));
+        // post op argument stride_z
+        for(size_t i = 0; i < _num_post_op_args; ++i)
+        {
+            const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(post_op_arg->info()->strides_in_bytes()[2]));
+        }
 
         // Cross-plan padding (if _reinterpret_input_as_3d = true)
         if(_reinterpret_input_as_3d && _has_pad_y)
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h b/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
index cb82b4a..a8f0c4c 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
@@ -90,13 +90,14 @@
     void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
 
 private:
-    bool _slide_matrix_b{ true };
-    bool _reinterpret_input_as_3d{ false };
-    bool _reinterpret_output_as_3d{ false };
-    bool _use_dummy_work_items{ false };
-    bool _add_bias{ false };
-    bool _export_to_cl_image{ false };
-    bool _has_pad_y{ false };
+    bool         _slide_matrix_b{ true };
+    bool         _reinterpret_input_as_3d{ false };
+    bool         _reinterpret_output_as_3d{ false };
+    bool         _use_dummy_work_items{ false };
+    bool         _add_bias{ false };
+    bool         _export_to_cl_image{ false };
+    bool         _has_pad_y{ false };
+    unsigned int _num_post_op_args{ 0 }; // (EXPERIMENTAL_POST_OPS) total number of post op arguments
 };
 } // namespace kernels
 } // namespace opencl
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp
index e05256e..50ecb21 100644
--- a/src/gpu/cl/operators/ClGemm.cpp
+++ b/src/gpu/cl/operators/ClGemm.cpp
@@ -204,7 +204,6 @@
 void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
                               const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
     DataType           data_type               = a->data_type();
     bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
     const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
@@ -223,6 +222,7 @@
     kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
     kernel_info.broadcast_bias          = broadcast_bias;
     kernel_info.activation_info         = gemm_info.activation_info();
+    kernel_info.post_ops                = gemm_info.post_ops();
 
     // Set the target for the kernels
     _mm_native_kernel->set_target(gpu_target);
@@ -281,7 +281,6 @@
 void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
                                          const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
     DataType           data_type               = a->data_type();
     bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
     const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
@@ -300,6 +299,7 @@
     kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
     kernel_info.broadcast_bias          = broadcast_bias;
     kernel_info.activation_info         = gemm_info.activation_info();
+    kernel_info.post_ops                = gemm_info.post_ops();
 
     // Set the target for the kernels
     _mm_reshaped_only_rhs_kernel->set_target(gpu_target);
@@ -334,7 +334,6 @@
 {
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
 
     // Get the GPU target
     const GPUTarget    gpu_target              = CLScheduler::get().target();
@@ -355,6 +354,7 @@
     kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
     kernel_info.broadcast_bias          = broadcast_bias;
     kernel_info.activation_info         = gemm_info.activation_info();
+    kernel_info.post_ops                = gemm_info.post_ops();
 
     auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
 
@@ -418,7 +418,6 @@
 {
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
 
     TensorInfo tmp_b_info{};
 
@@ -441,6 +440,7 @@
     kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
     kernel_info.broadcast_bias          = broadcast_bias;
     kernel_info.activation_info         = gemm_info.activation_info();
+    kernel_info.post_ops                = gemm_info.post_ops();
 
     GEMMLHSMatrixInfo lhs_info;
     GEMMRHSMatrixInfo rhs_info;
@@ -562,10 +562,9 @@
 
 void ClGemm::run(ITensorPack &tensors)
 {
-    const ITensor *lhs  = tensors.get_const_tensor(ACL_SRC_0);
-    const ITensor *rhs  = tensors.get_const_tensor(ACL_SRC_1);
-    const ITensor *src2 = tensors.get_const_tensor(ACL_SRC_2);
-    ITensor       *dst  = tensors.get_tensor(ACL_DST);
+    const ITensor *lhs = tensors.get_const_tensor(ACL_SRC_0);
+    const ITensor *rhs = tensors.get_const_tensor(ACL_SRC_1);
+    ITensor       *dst = tensors.get_tensor(ACL_DST);
 
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, dst);
 
@@ -620,7 +619,10 @@
             const unsigned int cross_plane_pad_dst = dst->info()->padding().top + dst->info()->padding().bottom;
             bool               has_pad_y           = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0);
 
-            ITensorPack gemm_reshaped_onlyrhs_pack{ { ACL_SRC_0, lhs }, { ACL_SRC_1, rhs_reshaped.get() }, { ACL_SRC_2, src2 }, { ACL_DST, dst } };
+            // Copy original tensor pack and overwrite rhs with reshaped counterpart
+            ITensorPack gemm_reshaped_onlyrhs_pack(tensors);
+            gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get());
+
             if(has_pad_y)
             {
                 CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_fallback_kernel, gemm_reshaped_onlyrhs_pack, true);
diff --git a/tests/framework/Macros.h b/tests/framework/Macros.h
index a6ba137..ac03bb0 100644
--- a/tests/framework/Macros.h
+++ b/tests/framework/Macros.h
@@ -49,8 +49,8 @@
 
 #define CONCAT(ARG0, ARG1) ARG0##ARG1
 
-#define VARIADIC_SIZE_IMPL(e0, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13, size, ...) size
-#define VARIADIC_SIZE(...) VARIADIC_SIZE_IMPL(__VA_ARGS__, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
+#define VARIADIC_SIZE_IMPL(e0, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13, e14, e15, size, ...) size
+#define VARIADIC_SIZE(...) VARIADIC_SIZE_IMPL(__VA_ARGS__, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
 
 #define JOIN_PARAM1(OP, param) OP(0, param)
 #define JOIN_PARAM2(OP, param, ...) \
@@ -92,6 +92,12 @@
 #define JOIN_PARAM14(OP, param, ...) \
     OP(13, param)                    \
     , JOIN_PARAM13(OP, __VA_ARGS__)
+#define JOIN_PARAM15(OP, param, ...) \
+    OP(14, param)                    \
+    , JOIN_PARAM14(OP, __VA_ARGS__)
+#define JOIN_PARAM16(OP, param, ...) \
+    OP(15, param)                    \
+    , JOIN_PARAM15(OP, __VA_ARGS__)
 #define JOIN_PARAM(OP, NUM, ...) \
     CONCAT(JOIN_PARAM, NUM)      \
     (OP, __VA_ARGS__)
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
index dc5fbc3..e3f151a 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -53,6 +53,11 @@
 template <typename T>
 using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
 
+// Fixture for CLGEMMMatrixMultiplyNative with post ops
+template <typename T>
+using CLGEMMMatrixMultiplyNativeWithPostOpsFixture =
+    GEMMMatrixMultiplyNativeWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
+
 // Fixture for CLGEMMMatrixMultiplyNative3D
 template <typename T>
 using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
@@ -141,6 +146,80 @@
                                     broadcast_bias_values),
                                     framework::dataset::make("Activation", ActivationLayerInfo()));
 
+/** Post Ops */
+using PostOpArgBroadcast =  CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>::PostOpArgBroadcast;
+experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(true, true, false),   // If broadcast in dims 0, 1 and 2
+        0,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    return post_ops;
+}
+experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(false, true, true),   // If broadcast in dims 0, 1 and 2
+        1,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    return post_ops;
+}
+experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    // post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(false, false, false),  // If broadcast in dims 0, 1 and 2
+        1,
+        ConvertPolicy::SATURATE);
+    return post_ops;
+}
+
+/** Different Post Op Lists */
+const auto post_op_lists = framework::dataset::make("post_op_lists", {
+    post_ops_1(),
+    post_ops_2(),
+    post_ops_3(),
+} );
+
+bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
+{
+    const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
+    const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
+
+    // Create TensorInfo for post op arguments
+    TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
+    TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
+    TensorInfo input2_info(TensorShape(n), 1, data_type);
+    TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
+
+    GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
+             false /**< reinterpret the input as 3D */,
+             true  /**< Flag used to broadcast the bias addition */,
+             false /**< wider accumm */,
+             false /**< has pad y */,
+           ActivationLayerInfo::ActivationFunction::IDENTITY,
+             1   /**< Multiplication factor for the width of the 1xW transposed block */,
+             1   /**< Multiplication factor for the height of the 4x4 interleaved block */,
+             lhs_info,
+             rhs_info,
+             0  /**< Offset to be added to each element of the matrix A */,
+             0 /**< Offset to be added to each element of the matrix B */,
+             post_ops);
+    return bool(ClGemmMatrixMultiplyNativeKernel::validate(&input0_info.clone()->set_is_resizable(true),
+                                                          &input1_info.clone()->set_is_resizable(true),
+                                                          &input2_info.clone()->set_is_resizable(true),
+                                                          &output_info.clone()->set_is_resizable(true),1.f,1.f,
+                                                          lhs_info,
+                                                          rhs_info,
+                                                          gemm_info));
+}
+
 /** Configuration test */
 void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info)
 {
@@ -191,6 +270,119 @@
 
 TEST_SUITE(CL)
 TEST_SUITE(GEMMMatrixMultiplyNative)
+TEST_SUITE(ValidateFusedPostOpsConfigs)
+TEST_SUITE(Invalid)
+TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 17;
+    const unsigned int n = 1;
+    const unsigned int k = 13;
+    const unsigned int batch = 2;
+    TensorShape post_op_arg0_shape(n, m, batch);
+    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
+    auto post_op_arg1_info = post_op_arg_info.clone();
+
+    // Unsupported sequence of post ops
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+        &post_op_arg_info,
+        1,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+        post_op_arg1_info.get(),
+        0,
+        ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
+{
+    // Invalid broadcast: post op tensors "widen" the output tensor
+    const auto data_type = DataType::F32;
+    const unsigned int m = 1;
+    const unsigned int n = 18;
+    const unsigned int k = 13;
+    const unsigned int batch = 2;
+    TensorShape post_op_arg_shape(n, m + 1, batch); // output's Y dimension (m) is "widened", which is not allowed
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
+{
+    // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, m, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Invalid
+TEST_SUITE(Valid)
+TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(n, 1, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, 1, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, 1, 1);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Valid
+TEST_SUITE_END() // ValidateFusedPostOps
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(
@@ -323,6 +515,32 @@
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
 }
+
+TEST_SUITE(FusedPostOps)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>, framework::DatasetMode::ALL,
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                                                                   m_values,
+                                                                   n_values),
+                                                                   k_values),
+                                                                   b_values),
+                                                                   framework::dataset::make("M0", { 4 })),
+                                                                   n0_values_precommit),
+                                                                   k0_values_precommit),
+                                                                   framework::dataset::make("DataType", DataType::F32)),
+                                                                   framework::dataset::make("alpha", {1.0f} )),
+                                                                   framework::dataset::make("beta", {1.0f} )),
+                                                                   framework::dataset::make("broadcast_bias", { false, true } )),
+                                                                   framework::dataset::make("Activation", { ActivationLayerInfo() })),
+                                                                   post_op_lists)
+                                                                   )
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+
+TEST_SUITE_END() //  FusedPostOps
+
 TEST_SUITE_END() // FP32
 TEST_SUITE_END() // Float
 TEST_SUITE_END() // GEMMMatrixMulipltyNative
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index 0f86a70..9e1a185 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -26,6 +26,7 @@
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "src/core/experimental/PostOp.h"
 #include "src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
 #include "src/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
 #include "tests/CL/CLAccessor.h"
@@ -61,6 +62,11 @@
 template <typename T>
 using CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture = GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>;
 
+// Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS with post ops
+template <typename T>
+using CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture =
+    GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>;
+
 namespace
 {
 // *INDENT-OFF*
@@ -157,6 +163,81 @@
                                     broadcast_bias_values),
                                     framework::dataset::make("Activation", ActivationLayerInfo()));
 
+/** Post Ops */
+using PostOpArgBroadcast =  CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>::PostOpArgBroadcast;
+experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(true, true, false),   // If broadcast in dims 0, 1 and 2
+        0,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    return post_ops;
+}
+experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(false, true, true),   // If broadcast in dims 0, 1 and 2
+        1,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    return post_ops;
+}
+experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
+{
+    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
+    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
+        std::make_tuple(false, false, true),  // If broadcast in dims 0, 1 and 2
+        1,
+        ConvertPolicy::SATURATE);
+    return post_ops;
+}
+
+/** Different Post Op Lists */
+const auto post_op_lists = framework::dataset::make("post_op_lists", {
+    post_ops_1(),
+    post_ops_2(),
+    post_ops_3(),
+ } );
+
+ bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
+{
+    const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
+    const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
+
+    // Create TensorInfo for post op arguments
+    TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
+    TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
+    TensorInfo input2_info(TensorShape(n), 1, data_type);
+    TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
+
+    const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info));
+
+    GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
+             false /**< reinterpret the input as 3D */,
+             true  /**< Flag used to broadcast the bias addition */,
+             false /**< wider accumm */,
+             false /**< has pad y */,
+           ActivationLayerInfo::ActivationFunction::IDENTITY,
+             1   /**< Multiplication factor for the width of the 1xW transposed block */,
+             1   /**< Multiplication factor for the height of the 4x4 interleaved block */,
+             lhs_info,
+             rhs_info,
+             0  /**< Offset to be added to each element of the matrix A */,
+             0 /**< Offset to be added to each element of the matrix B */,
+             post_ops);
+    return bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(&input0_info.clone()->set_is_resizable(true),
+                                                          &reshaped_input1_info.clone()->set_is_resizable(true),
+                                                          &input2_info.clone()->set_is_resizable(true),
+                                                          &output_info.clone()->set_is_resizable(true),1.f,1.f,
+                                                          lhs_info,
+                                                          rhs_info,
+                                                          gemm_info));
+}
 /** Configuration test */
 bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value,
                             unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, unsigned int h0_value,
@@ -211,6 +292,7 @@
     CLGEMMMatrixMultiplyReshapedOnlyRHS gemm;
     return bool(gemm.validate(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info));
 }
+
 } // namespace
 
 TEST_SUITE(CL)
@@ -262,6 +344,119 @@
     ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS);
 }
 
+TEST_SUITE(ValidateFusedPostOpsConfigs)
+TEST_SUITE(Invalid)
+TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 17;
+    const unsigned int n = 1;
+    const unsigned int k = 13;
+    const unsigned int batch = 2;
+    TensorShape post_op_arg0_shape(n, m, batch);
+    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
+    auto post_op_arg1_info = post_op_arg_info.clone();
+
+    // Unsupported sequence of post ops
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+        &post_op_arg_info,
+        1,
+        ConvertPolicy::SATURATE);
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
+        post_op_arg1_info.get(),
+        0,
+        ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
+{
+    // Invalid broadcast: post op tensors "widen" the output tensor
+    const auto data_type = DataType::F32;
+    const unsigned int m = 17;
+    const unsigned int n = 1;
+    const unsigned int k = 1;
+    const unsigned int batch = 1;
+    TensorShape post_op_arg_shape(n, m, batch + 4); // output's batch dimension is "widened", which is not allowed
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
+{
+    // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, m, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Invalid
+TEST_SUITE(Valid)
+TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(n, 1, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, 1, batch);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
+{
+    const auto data_type = DataType::F32;
+    const unsigned int m = 22;
+    const unsigned int n = 16;
+    const unsigned int k = 15;
+    const unsigned int batch = 3;
+    TensorShape post_op_arg_shape(1, 1, 1);
+    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
+    experimental::PostOpList<ITensorInfo*> post_ops{};
+    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
+
+    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Valid
+TEST_SUITE_END() // ValidateFusedPostOps
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
 
@@ -462,6 +657,44 @@
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
+
+TEST_SUITE(FusedPostOps)
+
+FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>, framework::DatasetMode::ALL,
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                                                                   m_values,
+                                                                   n_values),
+                                                                   k_values),
+                                                                   b_values),
+                                                                   m0_values_precommit),
+                                                                   n0_values_precommit),
+                                                                   k0_values_precommit),
+                                                                   framework::dataset::make("H0", {1})),
+                                                                   framework::dataset::make("interleave_rhs", { true })),
+                                                                   t_values_rhs),
+                                                                   framework::dataset::make("export_to_cl_image_rhs", false, true)),
+                                                                   framework::dataset::make("DataType", DataType::F32)),
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   framework::dataset::make("broadcast_bias", { false } )),
+                                                                   act_values),
+                                                                   post_op_lists)
+                                                                   )
+{
+    // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension
+    if(validate_result)
+    {
+        validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+    }
+    else
+    {
+        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
+        framework::ARM_COMPUTE_PRINT_INFO();
+    }
+}
+
+TEST_SUITE_END() //  FusedPostOps
+
 TEST_SUITE_END() // FP32
 
 TEST_SUITE(FP16)
@@ -590,6 +823,43 @@
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
+TEST_SUITE(FusedPostOps)
+
+FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<half>, framework::DatasetMode::ALL,
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                                                                   m_values,
+                                                                   n_values),
+                                                                   k_values),
+                                                                   b_values),
+                                                                   m0_values_precommit),
+                                                                   n0_values_precommit),
+                                                                   k0_values_precommit),
+                                                                   framework::dataset::make("H0", {1})),
+                                                                   framework::dataset::make("interleave_rhs", { true })),
+                                                                   t_values_rhs),
+                                                                   framework::dataset::make("export_to_cl_image_rhs", true)),
+                                                                   framework::dataset::make("DataType", DataType::F16)),
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   framework::dataset::make("broadcast_bias", { false } )),
+                                                                   act_values),
+                                                                   post_op_lists)
+                                                                   )
+{
+    // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension
+    if(validate_result)
+    {
+        validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16);
+    }
+    else
+    {
+        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
+        framework::ARM_COMPUTE_PRINT_INFO();
+    }
+}
+
+TEST_SUITE_END() //  FusedPostOps
+
 TEST_SUITE_END() // FP16
 
 TEST_SUITE_END() // Float
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index e119158..fa27301 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -1522,6 +1522,243 @@
     SimpleTensor<T> _reference{};
 };
 
+/** (EXPERIMENTAL_POST_OPS)*/
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
+class GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture : public framework::Fixture
+{
+public:
+    using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument
+    template <typename...>
+    void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0,
+               bool interleave_rhs, bool transpose_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info,
+               const experimental::PostOpList<PostOpArgBroadcast> &post_ops)
+    {
+        GEMMLHSMatrixInfo lhs_info;
+        lhs_info.m0 = m0;
+        lhs_info.k0 = k0;
+
+        GEMMRHSMatrixInfo rhs_info;
+        rhs_info.n0                 = n0;
+        rhs_info.k0                 = k0;
+        rhs_info.h0                 = h0;
+        rhs_info.interleave         = interleave_rhs;
+        rhs_info.transpose          = transpose_rhs;
+        rhs_info.export_to_cl_image = export_to_cl_image;
+
+        // Set the tensor shapes for LHS and RHS matrices
+        const TensorShape lhs_shape(k, m, batch_size);
+        const TensorShape rhs_shape(n, k, batch_size);
+        const TensorShape bias_shape(n,
+                                     broadcast_bias ? 1 : m,
+                                     broadcast_bias ? 1 : batch_size);
+        auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops,
+                                                                                                                    [ = ](auto broadcast)
+        {
+            return TensorShape
+            {
+                std::get<0>(broadcast) ? 1 : n,
+                std::get<1>(broadcast) ? 1 : m,
+                std::get<2>(broadcast) ? 1 : batch_size,
+            };
+        });
+
+        _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+        if(validate_result)
+        {
+            _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+        }
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported.");
+        using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
+
+        DistributionType distribution{ T(-1.0f), T(1.0f) };
+        library->fill(tensor, distribution, i);
+
+        // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+        DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) };
+        library->fill_borders_with_garbage(tensor, distribution_inf, i);
+    }
+
+    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+                              DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+    {
+        // Create tensors
+        TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
+        TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
+        TensorType rhs_reshaped;
+        TensorType dst;
+        // Create post op tensors and populate post op with them
+        std::vector<TensorType> post_op_tensors_holder{};
+        auto                    populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops,
+                                                                                                                                [&post_op_tensors_holder, &data_type](auto shape)
+        {
+            auto t = create_tensor<TensorType>(shape, data_type, 1);
+            post_op_tensors_holder.push_back(std::move(t));
+            return post_op_tensors_holder.back().info();
+        });
+
+        const unsigned int M = lhs_shape[1];
+        const unsigned int N = rhs_shape[0];
+        const unsigned int K = lhs_shape[0];
+        GEMMKernelInfo     kernel_info;
+        kernel_info.m                       = M;
+        kernel_info.n                       = N;
+        kernel_info.k                       = K;
+        kernel_info.depth_output_gemm3d     = 0;
+        kernel_info.reinterpret_input_as_3d = false;
+        kernel_info.broadcast_bias          = broadcast_bias;
+        kernel_info.activation_info         = act_info;
+        kernel_info.post_ops                = populated_post_ops;
+
+        // The output tensor will be auto-initialized within the function
+
+        // Create and configure function
+        ReshapeRHSOperatorType reshape_rhs;
+        GEMMOperatorType       gemm;
+
+        validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
+        validate_result = validate_result || !rhs_info.export_to_cl_image;
+        if(!validate_result)
+        {
+            return nullptr;
+        }
+
+        reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+        gemm.configure(lhs.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+
+        ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
+        for(const auto &tensor : post_op_tensors_holder)
+        {
+            ARM_COMPUTE_ASSERT(tensor.info()->is_resizable());
+        }
+
+        // We do not pad when using image as it needs to comply to strict pitch alignment restrictions
+        if(!rhs_info.export_to_cl_image)
+        {
+            add_padding_x({ &lhs, &rhs, &rhs_reshaped, &bias, &dst });
+            for(auto &tensor : post_op_tensors_holder)
+            {
+                add_padding_x({ &tensor });
+            }
+        }
+
+        // Allocate tensors
+        lhs.allocator()->allocate();
+        rhs.allocator()->allocate();
+        rhs_reshaped.allocator()->allocate();
+        bias.allocator()->allocate();
+        dst.allocator()->allocate();
+        for(auto &tensor : post_op_tensors_holder)
+        {
+            tensor.allocator()->allocate();
+        }
+
+        ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
+        for(const auto &tensor : post_op_tensors_holder)
+        {
+            ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable());
+        }
+
+        // Fill tensors
+        fill(AccessorType(lhs), 0);
+        fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
+        for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+        {
+            fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i);
+        }
+
+        // Compute GEMM
+        ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+        reshape_rhs.run(reshape_rhs_pack);
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs_reshaped },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+        {
+            gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i));
+        }
+        gemm.run(gemm_pack);
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+                                      const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+    {
+        TensorShape dst_shape = lhs_shape;
+        dst_shape[0]          = rhs_shape[0];
+        dst_shape[1]          = lhs_shape[1];
+
+        // Create reference
+        SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
+        SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_shape, data_type, 1 };
+        // Create post op tensors and populate post op with them
+        auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape)
+        {
+            return SimpleTensor<T> { shape, data_type, 1 };
+        });
+
+        const int n          = rhs_shape[0];
+        const int m          = lhs_shape[1];
+        const int batch_size = lhs_shape[2];
+
+        // Fill reference
+        int tensor_idx = 0;
+        fill(lhs, tensor_idx++);
+        fill(rhs, tensor_idx++);
+        fill(bias, tensor_idx++);
+        for(auto &op : populated_post_ops.get_list())
+        {
+            for(auto tensor : op->arguments())
+            {
+                fill(*tensor, tensor_idx++);
+            }
+        }
+
+        if(broadcast_bias)
+        {
+            // In case of broadcast, we need simply copy the first into the following "M" ones
+            for(int i = 1; i < m * batch_size; i++)
+            {
+                memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
+            }
+        }
+
+        SimpleTensor<T> out;
+        out = reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+        // Ignore activation info if post ops are used instead
+        if(populated_post_ops.size() > 0)
+        {
+            out = reference::post_ops<T>(out, populated_post_ops);
+        }
+        else
+        {
+            out = reference::activation_layer(out, act_info);
+        }
+        return out;
+    }
+
+    bool            validate_result = true;
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+
 template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSOperatorType, typename GEMMOperatorType>
 class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
 {
@@ -1830,6 +2067,213 @@
 };
 
 template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
+class GEMMMatrixMultiplyNativeWithPostOpsValidationFixture : public framework::Fixture
+{
+public:
+    using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument
+public:
+    template <typename...>
+    void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias,
+               const ActivationLayerInfo &act_info, const experimental::PostOpList<PostOpArgBroadcast> &post_ops)
+    {
+        GEMMLHSMatrixInfo lhs_info;
+        lhs_info.m0 = m0;
+        lhs_info.k0 = k0;
+
+        GEMMRHSMatrixInfo rhs_info;
+        rhs_info.n0 = n0;
+        rhs_info.k0 = k0;
+
+        // Set the tensor shapes for LHS and RHS matrices
+        const TensorShape lhs_shape(k, m, batch_size);
+        const TensorShape rhs_shape(n, k, batch_size);
+        const TensorShape bias_shape(n,
+                                     broadcast_bias ? 1 : m,
+                                     broadcast_bias ? 1 : batch_size);
+        const auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops,
+                                                                                                                          [ = ](auto broadcast)
+        {
+            return TensorShape
+            {
+                std::get<0>(broadcast) ? 1 : n,
+                std::get<1>(broadcast) ? 1 : m,
+                std::get<2>(broadcast) ? 1 : batch_size,
+            };
+        });
+
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported.");
+        using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
+
+        DistributionType distribution{ T(-1.0f), T(1.0f) };
+        library->fill(tensor, distribution, i);
+
+        // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+        DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) };
+        library->fill_borders_with_garbage(tensor, distribution_inf, i);
+    }
+
+    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+                              DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+    {
+        // Create tensors
+        TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
+        TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
+        TensorType dst;
+        // Create post op tensors and populate post op with them
+        std::vector<TensorType> post_op_tensors_holder{};
+        auto                    populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops,
+                                                                                                                                [&post_op_tensors_holder, &data_type](auto shape)
+        {
+            auto t = create_tensor<TensorType>(shape, data_type, 1);
+            post_op_tensors_holder.push_back(std::move(t));
+            return post_op_tensors_holder.back().info();
+        });
+
+        const unsigned int M = lhs_shape[1];
+        const unsigned int N = rhs_shape[0];
+        const unsigned int K = lhs_shape[0];
+        GEMMKernelInfo     kernel_info;
+        kernel_info.m                       = M;
+        kernel_info.n                       = N;
+        kernel_info.k                       = K;
+        kernel_info.depth_output_gemm3d     = 0;
+        kernel_info.reinterpret_input_as_3d = false;
+        kernel_info.broadcast_bias          = broadcast_bias;
+        kernel_info.activation_info         = act_info;
+        kernel_info.post_ops                = populated_post_ops;
+
+        // Create and configure function
+        GEMMOperatorType gemm;
+        gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+
+        ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
+        for(const auto &tensor : post_op_tensors_holder)
+        {
+            ARM_COMPUTE_ASSERT(tensor.info()->is_resizable());
+        }
+
+        add_padding_x({ &lhs, &rhs, &bias, &dst });
+        for(auto &tensor : post_op_tensors_holder)
+        {
+            add_padding_x({ &tensor });
+        }
+
+        // Allocate tensors
+        lhs.allocator()->allocate();
+        rhs.allocator()->allocate();
+        bias.allocator()->allocate();
+        dst.allocator()->allocate();
+        for(auto &tensor : post_op_tensors_holder)
+        {
+            tensor.allocator()->allocate();
+        }
+
+        ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
+        ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
+        for(const auto &tensor : post_op_tensors_holder)
+        {
+            ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable());
+        }
+
+        // Fill tensors
+        fill(AccessorType(lhs), 0);
+        fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
+        for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+        {
+            fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i);
+        }
+
+        // Compute GEMM
+        ITensorPack gemm_pack({ { ACL_SRC_0, &lhs },
+            { ACL_SRC_1, &rhs },
+            { ACL_SRC_2, &bias },
+            { ACL_DST, &dst }
+        });
+        for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+        {
+            gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i));
+        }
+        gemm.run(gemm_pack);
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+                                      const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+    {
+        TensorShape dst_shape = lhs_shape;
+        dst_shape[0]          = rhs_shape[0];
+        dst_shape[1]          = lhs_shape[1];
+
+        // Create reference
+        SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
+        SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_shape, data_type, 1 };
+        // Create post op tensors and populate post op with them
+        auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape)
+        {
+            return SimpleTensor<T> { shape, data_type, 1 };
+        });
+
+        const int n          = rhs_shape[0];
+        const int m          = lhs_shape[1];
+        const int batch_size = lhs_shape[2];
+
+        // Fill reference
+        int tensor_idx = 0;
+        fill(lhs, tensor_idx++);
+        fill(rhs, tensor_idx++);
+        fill(bias, tensor_idx++);
+        for(auto &op : populated_post_ops.get_list())
+        {
+            for(auto tensor : op->arguments())
+            {
+                fill(*tensor, tensor_idx++);
+            }
+        }
+
+        if(broadcast_bias)
+        {
+            // In case of broadcast, we need simply copy the first into the following "M" ones
+            for(int i = 1; i < m * batch_size; i++)
+            {
+                memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
+            }
+        }
+
+        SimpleTensor<T> out;
+        out = reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+        // Ignore activation info if post ops are used instead
+        if(populated_post_ops.size() > 0)
+        {
+            out = reference::post_ops<T>(out, populated_post_ops);
+        }
+        else
+        {
+            out = reference::activation_layer(out, act_info);
+        }
+        return out;
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+
+template <typename TensorType, typename AccessorType, typename T, typename GEMMOperatorType>
 class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
 {
 public: