COMPMID-2053: Fuse bias addition with CLGEMMMatrixMultiplyReshapedKernel

Change-Id: I5bfd38c94a6fd18a1cba2104f7e1b04e7bef6ec2
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1359
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

10 files changed, 416 insertions, 236 deletions

diff --git a/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
index 4052a09162..68ab94a31d 100644
--- a/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
@@ -51,8 +51,10 @@ public:
      *
      * @param[in]  input0    Input tensor containing the LHS reshaped matrix. Data type supported: F32/F16. The number of dimensions for the LHS matrix must be less or equal than 4
      * @param[in]  input1    Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3
+     * @param[in]  input2    Input tensor containing the bias matrix. Data type supported: same as @p input0.
      * @param[out] output    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
      * @param[in]  alpha     Weight of the matrix product
+     * @param[in]  beta      Weight of the matrix bias
      * @param[in]  lhs_info  LHS matrix information used for reshaping the input0 tensor.  Only the following values are supported:
      *                       lhs_info.m0: 2,3,4,5,6,7,8
      *                       lhs_info.k0: 2,3,4,8,16
@@ -65,14 +67,17 @@ public:
      *
      * @note lhs_info.k0 must be equal to rhs_info.k0
      */
-    void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
-                   const GEMMReshapeInfo &gemm_info);
+    void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+                   const GEMMRHSMatrixInfo &rhs_info,
+                   const GEMMReshapeInfo   &gemm_info);
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyReshapedKernel
      *
      * @param[in] input0    Input tensor containing the LHS reshaped matrix. Data type supported: F32/F16. The number of dimensions for the LHS matrix must be less or equal than 4
      * @param[in] input1    Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3
+     * @param[in] input2    Input tensor info containing the bias matrix. Data type supported: same as @p input0.
      * @param[in] output    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
      * @param[in] alpha     Weight of the matrix product
+     * @param[in] beta      Weight of the matrix bias
      * @param[in] lhs_info  LHS matrix information used for reshaping the input0 tensor.  Only the following values are supported:
      *                      lhs_info.m0: 2,3,4,5,6,7,8
      *                      lhs_info.k0: 2,3,4,8,16
@@ -87,8 +92,9 @@ public:
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
-                           const GEMMReshapeInfo &gemm_info);
+    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+                           const GEMMRHSMatrixInfo &rhs_info,
+                           const GEMMReshapeInfo   &gemm_info);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
@@ -96,11 +102,14 @@ public:
 private:
     const ICLTensor *_input0;
     const ICLTensor *_input1;
+    const ICLTensor *_input2;
     ICLTensor       *_output;
     bool             _slide_matrix_b;
     bool             _reinterpret_output_as_3d;
     unsigned int     _k;
     bool             _use_dummy_work_items;
+    bool             _add_bias;
+    bool             _broadcast_bias;
 };
 } // namespace arm_compute
 #endif /*__ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H__*/
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/gemm.cl b/src/core/CL/cl_kernels/gemm.cl
index 2ac2eb7c32..7ada14c774 100644
--- a/src/core/CL/cl_kernels/gemm.cl
+++ b/src/core/CL/cl_kernels/gemm.cl
@@ -1042,11 +1042,12 @@ __kernel void gemm_reshape_rhs_matrix_t(TENSOR3D_DECLARATION(src),
  * @param[in]  rhs_step_y                         src_stride_y * number of elements along Y processed per workitem(in bytes)
  * @param[in]  rhs_offset_first_element_in_bytes  The offset of the first element in the RHS reshaped matrix
  * @param[in]  bias_ptr                           (Optional)Pointer to the bias reshaped matrix. Supported data type: same as @p lhs_ptr
- * @param[in]  bias_stride_x                      (Optional)Stride of the bias reshaped matrix in X dimension (in bytes)
- * @param[in]  bias_step_x                        (Optional)bias_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  bias_stride_y                      (Optional)Stride of the bias reshaped matrix in Y dimension (in bytes)
- * @param[in]  bias_step_y                        (Optional)bias_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  bias_offset_first_element_in_bytes (Optional)The offset of the first element in the bias reshaped matrix
+ * @param[in]  bias_ptr                           (Optional) Pointer to the bias matrix. Supported data type: same as @p lhs_ptr
+ * @param[in]  bias_stride_x                      (Optional) Stride of the bias matrix in X dimension (in bytes)
+ * @param[in]  bias_step_x                        (Optional) bias_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  bias_stride_y                      (Optional) Stride of the bias matrix in Y dimension (in bytes)
+ * @param[in]  bias_step_y                        (Optional) bias_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix
  * @param[out] dst_ptr                            Pointer to the destination matrix Supported data type: same as @p lhs_ptr
  * @param[in]  dst_stride_x                       Stride of the destination matrix in X dimension (in bytes)
  * @param[in]  dst_step_x                         dst_stride_x * number of elements along X processed per workitem(in bytes)
@@ -1055,7 +1056,7 @@ __kernel void gemm_reshape_rhs_matrix_t(TENSOR3D_DECLARATION(src),
  * @param[in]  dst_offset_first_element_in_bytes  The offset of the first element in the destination matrix
  * @param[in]  lhs_stride_z                       Stride of the LHS reshaped matrix in Z dimension (in bytes)
  * @param[in]  rhs_stride_z                       Stride of the RHS reshaped matrix in Z dimension (in bytes)
- * @param[in]  bias_stride_z                      (Optional) Stride of the bias  matrix in Z dimension (in bytes)
+ * @param[in]  bias_stride_z                      (Optional) Stride of the bias matrix in Z dimension (in bytes)
  * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
  * @param[in]  lhs_cross_plane_pad                (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D)
  * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
@@ -1415,10 +1416,10 @@ __kernel void gemm_mm_reshaped_only_rhs_t(IMAGE_DECLARATION(lhs),
  * @param[in]  rhs_stride_y                       Stride of the RHS reshaped matrix in Y dimension (in bytes)
  * @param[in]  rhs_step_y                         src_stride_y * number of elements along Y processed per workitem(in bytes)
  * @param[in]  rhs_offset_first_element_in_bytes  The offset of the first element in the RHS reshaped matrix
- * @param[in]  bias_ptr                           (Optional) Pointer to the bias reshaped matrix. Supported data type: same as @p lhs_ptr
- * @param[in]  bias_stride_x                      (Optional) Stride of the bias reshaped matrix in X dimension (in bytes)
+ * @param[in]  bias_ptr                           (Optional) Pointer to the bias matrix. Supported data type: same as @p lhs_ptr
+ * @param[in]  bias_stride_x                      (Optional) Stride of the bias matrix in X dimension (in bytes)
  * @param[in]  bias_step_x                        (Optional) bias_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  bias_stride_y                      (Optional) Stride of the bias reshaped matrix in Y dimension (in bytes)
+ * @param[in]  bias_stride_y                      (Optional) Stride of the bias matrix in Y dimension (in bytes)
  * @param[in]  bias_step_y                        (Optional) bias_stride_y * number of elements along Y processed per workitem(in bytes)
  * @param[in]  bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix
  * @param[out] dst_ptr                            Pointer to the destination matrix Supported data type: same as @p lhs_ptr
@@ -1429,7 +1430,7 @@ __kernel void gemm_mm_reshaped_only_rhs_t(IMAGE_DECLARATION(lhs),
  * @param[in]  dst_offset_first_element_in_bytes  The offset of the first element in the destination matrix
  * @param[in]  lhs_stride_z                       Stride of the LHS reshaped matrix in Z dimension (in bytes)
  * @param[in]  rhs_stride_z                       Stride of the RHS reshaped matrix in Z dimension (in bytes)
- * @param[in]  bias_stride_z                      (Optional)Stride of the bias reshaped matrix in Z dimension (in bytes)
+ * @param[in]  bias_stride_z                      (Optional) Stride of the bias matrix in Z dimension (in bytes)
  * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
  * @param[in]  lhs_cross_plane_pad                (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D)
  * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
@@ -1804,36 +1805,49 @@ __kernel void gemm_mm_reshaped_only_rhs_nt(IMAGE_DECLARATION(lhs),
  *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
  *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix NOT reshaped
  *
- * @param[in]  lhs_ptr                           Pointer to the LHS reshaped matrix. Supported data type: F16/F32
- * @param[in]  lhs_stride_x                      Stride of the LHS reshaped matrix in X dimension (in bytes)
- * @param[in]  lhs_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  lhs_stride_y                      Stride of the LHS reshaped matrix in Y dimension (in bytes)
- * @param[in]  lhs_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  lhs_offset_first_element_in_bytes The offset of the first element in the LHS reshaped matrix
- * @param[in]  rhs_ptr                           Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr
- * @param[in]  rhs_stride_x                      Stride of the RHS reshaped matrix in X dimension (in bytes)
- * @param[in]  rhs_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  rhs_stride_y                      Stride of the RHS reshaped matrix in Y dimension (in bytes)
- * @param[in]  rhs_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix
- * @param[out] dst_ptr                           Pointer to the destination matrix Supported data type: same as @p lhs_ptr
- * @param[in]  dst_stride_x                      Stride of the destination matrix in X dimension (in bytes)
- * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  dst_stride_y                      Stride of the destination matrix in Y dimension (in bytes)
- * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- * @param[in]  k                                 Number of columns in LHS matrix and rows in RHS matrix not reshaped.
- * @param[in]  lhs_stride_z                      Stride of the LHS reshaped matrix in Z dimension (in bytes)
- * @param[in]  rhs_stride_z                      Stride of the RHS reshaped matrix in Z dimension (in bytes)
- * @param[in]  dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
- * @param[in]  dst_cross_plane_pad               (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
+ * @param[in]  lhs_ptr                            Pointer to the LHS reshaped matrix. Supported data type: F16/F32
+ * @param[in]  lhs_stride_x                       Stride of the LHS reshaped matrix in X dimension (in bytes)
+ * @param[in]  lhs_step_x                         src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  lhs_stride_y                       Stride of the LHS reshaped matrix in Y dimension (in bytes)
+ * @param[in]  lhs_step_y                         src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  lhs_offset_first_element_in_bytes  The offset of the first element in the LHS reshaped matrix
+ * @param[in]  rhs_ptr                            Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr
+ * @param[in]  rhs_stride_x                       Stride of the RHS reshaped matrix in X dimension (in bytes)
+ * @param[in]  rhs_step_x                         src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  rhs_stride_y                       Stride of the RHS reshaped matrix in Y dimension (in bytes)
+ * @param[in]  rhs_step_y                         src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  rhs_offset_first_element_in_bytes  The offset of the first element in the RHS reshaped matrix
+ * @param[in]  bias_ptr                           (Optional) Pointer to the bias matrix. Supported data type: same as @p lhs_ptr
+ * @param[in]  bias_stride_x                      (Optional) Stride of the bias matrix in X dimension (in bytes)
+ * @param[in]  bias_step_x                        (Optional) bias_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  bias_stride_y                      (Optional) Stride of the bias matrix in Y dimension (in bytes)
+ * @param[in]  bias_step_y                        (Optional) bias_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix
+ * @param[out] dst_ptr                            Pointer to the destination matrix Supported data type: same as @p lhs_ptr
+ * @param[in]  dst_stride_x                       Stride of the destination matrix in X dimension (in bytes)
+ * @param[in]  dst_step_x                         dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
+ * @param[in]  dst_step_y                         dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes  The offset of the first element in the destination matrix
+ * @param[in]  k                                  Number of columns in LHS matrix and rows in RHS matrix not reshaped.
+ * @param[in]  lhs_stride_z                       Stride of the LHS reshaped matrix in Z dimension (in bytes)
+ * @param[in]  rhs_stride_z                       Stride of the RHS reshaped matrix in Z dimension (in bytes)
+ * @param[in]  bias_stride_z                      (Optional) Stride of the bias matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
                                             IMAGE_DECLARATION(rhs),
+#if defined(BETA)
+                                            IMAGE_DECLARATION(bias),
+#endif // defined(BETA)
                                             IMAGE_DECLARATION(dst),
                                             uint k,
                                             uint lhs_stride_z,
                                             uint rhs_stride_z,
+#if defined(BETA)
+                                            uint bias_stride_z,
+#endif //defined(BETA)
                                             uint dst_stride_z
 #if defined(REINTERPRET_OUTPUT_AS_3D)
                                             ,
@@ -1892,8 +1906,8 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
     // 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;
 
-    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, zrhs, 0);
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zlhs, 0); //uint zlhs0=0,zlhs1=0,zlhs2=0,... zlhs7=0;
+    REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0);
 
     for(int i = 0; i < k; i += K0)
     {
@@ -1910,7 +1924,7 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
         LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_addr, 0, LHS_STEP_X * sizeof(DATA_TYPE), zlhs);
 
         // Load values from RHS matrix
-        LOAD_BLOCK(N0, K0, DATA_TYPE, b, rhs_addr, 0, RHS_STEP_X * sizeof(DATA_TYPE), zrhs);
+        LOAD_BLOCK(N0, K0, DATA_TYPE, b, rhs_addr, 0, RHS_STEP_X * sizeof(DATA_TYPE), zero);
 
         // Accumulate
         ARM_DOT_K0XN0(a0, b, c0);
@@ -1942,7 +1956,7 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
 
     __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE)) + (get_global_id(1) * (uint)M0 * dst_stride_y);
 
-    REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+    REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0);
 
 #if defined(REINTERPRET_OUTPUT_AS_3D)
 
@@ -1964,8 +1978,39 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
     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(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero);
+
+#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(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero);
+
+#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)
+
     // Store output block
     STORE_BLOCK(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout);
+
 #undef LHS_BLOCK_SIZE
 #undef LHS_OFFSET_X
 #undef LHS_STEP_X
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
index 59afa47f6f..4436726852 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
@@ -56,8 +56,9 @@ namespace
 {
 using ElementsProcessed = Steps;
 
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
-                          const GEMMReshapeInfo &gemm_info)
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+                          const GEMMRHSMatrixInfo &rhs_info,
+                          const GEMMReshapeInfo   &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
@@ -86,6 +87,22 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
     tensor_shape1.set(0, n);
     tensor_shape1.set(1, k);
 
+    if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+    {
+        const int input2_dim0 = static_cast<int>(input2->dimension(0));
+        const int input2_dim1 = static_cast<int>(input2->dimension(1));
+
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+        if(gemm_info.broadcast_bias())
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+        }
+    }
+
     const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
     const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
 
@@ -105,7 +122,8 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
+                                                        const GEMMRHSMatrixInfo &rhs_info,
                                                         const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)
 {
     unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
@@ -152,8 +170,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
                                      ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
                                      output->dimension(1) + bottom_pad);
 
-    window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
-                     update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
+    if(input2 != nullptr)
+    {
+        const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
+
+        const int bias_processed_per_iteration_y = gemm_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y;
+
+        AccessWindowStatic input2_access(input2, 0, 0,
+                                         ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
+                                         ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y));
+
+        window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
+                         update_window_and_padding(win_out, output_access);                             // window used to update the padding requirements of output tensor
+    }
+    else
+    {
+        window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
+                         update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
+    }
 
     output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
 
@@ -169,23 +203,28 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
 } // namespace
 
 CLGEMMMatrixMultiplyReshapedKernel::CLGEMMMatrixMultiplyReshapedKernel()
-    : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _k(1), _use_dummy_work_items(false)
+    : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _k(1), _use_dummy_work_items(false), _add_bias(false),
+      _broadcast_bias(false)
 {
 }
 
-void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+                                                   const GEMMLHSMatrixInfo &lhs_info,
                                                    const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
 
     _input0                   = input0;
     _input1                   = input1;
+    _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
     _output                   = output;
     _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
     _k                        = gemm_info.k();
     _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
+    _add_bias                 = _input2 != nullptr;
+    _broadcast_bias           = gemm_info.broadcast_bias();
 
     // Check if we need to slide the matrix B
     const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
@@ -194,7 +233,7 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, cons
     ElementsProcessed num_elements_processed{};
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
@@ -202,9 +241,12 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, cons
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
+    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
+    build_opts.add_option_if(gemm_info.broadcast_bias(), "-DBROADCAST_BIAS");
     build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
     build_opts.add_option_if(lhs_info.interleave, "-DLHS_INTERLEAVE");
     build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
@@ -227,6 +269,8 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, cons
     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
+    _config_id += (_add_bias ? "add_bias_" : "");
+    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
     _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
     _config_id += "_";
@@ -253,13 +297,15 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, cons
     _config_id += support::cpp11::to_string(rhs_info.interleave);
 }
 
-Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+                                                    const GEMMLHSMatrixInfo &lhs_info,
                                                     const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
 {
     ElementsProcessed num_elements_processed{};
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, lhs_info, rhs_info, gemm_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
                                                               input1->clone().get(),
+                                                              input2 != nullptr ? input2->clone().get() : nullptr,
                                                               output->clone().get(),
                                                               lhs_info,
                                                               rhs_info,
@@ -290,7 +336,15 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu
     if(_reinterpret_output_as_3d)
     {
         // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
-        const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 4;
+        unsigned int idx0;
+        if(_add_bias)
+        {
+            idx0 = 4 * num_arguments_per_2D_tensor() + 5;
+        }
+        else
+        {
+            idx0 = 3 * num_arguments_per_2D_tensor() + 4;
+        }
         const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
         _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
     }
@@ -308,10 +362,18 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu
         unsigned int idx = 0;
         add_2D_tensor_argument(idx, _input0, slice);
         add_2D_tensor_argument(idx, _input1, slice_b);
+        if(_add_bias)
+        {
+            add_2D_tensor_argument(idx, _input2, slice);
+        }
         add_2D_tensor_argument(idx, _output, slice);
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_k));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+        if(_add_bias)
+        {
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
+        }
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
         enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
     }
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
index 99b3d20953..d952de8232 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
@@ -250,7 +250,7 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
     build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
-    build_opts.add_option_if(!(helpers::float_ops::is_zero(beta)) && _input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+    build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
     build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
     build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
@@ -277,6 +277,8 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input
     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
+    _config_id += (_add_bias ? "add_bias_" : "");
+    _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
     _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
     _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
     _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 21a9fce233..94b318c93e 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -34,6 +34,7 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/helpers/float_ops.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/ITensorAllocator.h"
@@ -189,10 +190,6 @@ void CLGEMM::configure_reshaped_v1(const ICLTensor *a, const ICLTensor *b, const
 
 void CLGEMM::configure_reshaped_v2(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON(c != nullptr);
-    ARM_COMPUTE_UNUSED(beta);
-    ARM_COMPUTE_UNUSED(c);
-
     DataType           data_type               = a->info()->data_type();
     bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
     const unsigned int m                       = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
@@ -201,12 +198,13 @@ void CLGEMM::configure_reshaped_v2(const ICLTensor *a, const ICLTensor *b, const
     const unsigned int batch_size              = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
     const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
     const GPUTarget    gpu_target              = CLScheduler::get().target();
+    bool               broadcast_bias          = gemm_info.broadcast_bias();
 
     // Set the target for the kernels
     _reshape_lhs_kernel.set_target(gpu_target);
     _mm_kernel.set_target(gpu_target);
 
-    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, depth_output_gemm3d, false);
+    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, depth_output_gemm3d, false, broadcast_bias);
 
     // Manage intermediate buffers
     _memory_group.manage(&_tmp_a);
@@ -230,7 +228,7 @@ void CLGEMM::configure_reshaped_v2(const ICLTensor *a, const ICLTensor *b, const
     _reshape_rhs_kernel.configure(b, &_tmp_b, rhs_info);
 
     // Configure and tune matrix multiply kernel
-    _mm_reshaped_kernel.configure(&_tmp_a, &_tmp_b, output, alpha, lhs_info, rhs_info, reshape_info);
+    _mm_reshaped_kernel.configure(&_tmp_a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, reshape_info);
 
     // Allocate intermediate tensors
     _tmp_a.allocator()->allocate();
@@ -395,9 +393,9 @@ Status CLGEMM::validate_reshaped_v2(const ITensorInfo *a, const ITensorInfo *b,
     const unsigned int k                       = a->dimension(0);
     const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
     const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-    const bool         add_c                   = (beta != 0.f && c != nullptr);
+    const bool         broadcast_bias          = gemm_info.broadcast_bias();
 
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, false);
+    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, false, broadcast_bias);
 
     GEMMLHSMatrixInfo lhs_info;
     GEMMRHSMatrixInfo rhs_info;
@@ -416,13 +414,8 @@ Status CLGEMM::validate_reshaped_v2(const ITensorInfo *a, const ITensorInfo *b,
     ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
 
     // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, output, alpha, lhs_info, rhs_info, reshape_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, reshape_info));
 
-    if(add_c)
-    {
-        // Validate matrix addition kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAdditionKernel::validate(c, output, beta));
-    }
     return Status{};
 }
 
@@ -486,31 +479,32 @@ void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *
     // Select GEMMType
     _gemm_type = select_gemm_type(m, n, k, a->info()->data_type(), _reshape_b_only_on_first_run, gpu_target);
 
-    const bool is_gemm_reshaped_only_rhs = _gemm_type == GEMMType::RESHAPED_ONLY_RHS;
-    const bool add_c                     = (beta != 0.f && c != nullptr);
-    const bool is_beta_one               = std::abs(1.0f - beta) < 0.00001f;
-    const bool fuse_add                  = (is_beta_one && (c != nullptr && c->info()->num_dimensions() == 1)) || is_gemm_reshaped_only_rhs;
+    const bool is_fuse_add_c_supported = (_gemm_type == GEMMType::RESHAPED_V2) || (_gemm_type == GEMMType::RESHAPED_ONLY_RHS);
+    const bool add_c                   = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
+    const bool fuse_add_c              = add_c && is_fuse_add_c_supported;
+
+    const ICLTensor *c_to_use = fuse_add_c ? c : nullptr;
 
     switch(_gemm_type)
     {
         case GEMMType::NATIVE:
         {
-            configure_native(a, b, (add_c && fuse_add) ? c : nullptr, output, alpha, beta, gemm_info);
+            configure_native(a, b, c_to_use, output, alpha, beta, gemm_info);
             break;
         }
         case GEMMType::RESHAPED_V1:
         {
-            configure_reshaped_v1(a, b, (add_c && fuse_add) ? c : nullptr, output, alpha, beta, gemm_info);
+            configure_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info);
             break;
         }
         case GEMMType::RESHAPED_V2:
         {
-            configure_reshaped_v2(a, b, (add_c && fuse_add) ? c : nullptr, output, alpha, beta, gemm_info);
+            configure_reshaped_v2(a, b, c_to_use, output, alpha, beta, gemm_info);
             break;
         }
         case GEMMType::RESHAPED_ONLY_RHS:
         {
-            configure_reshaped_only_rhs(a, b, (add_c && fuse_add) ? c : nullptr, output, alpha, beta, gemm_info);
+            configure_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info);
             break;
         }
         default:
@@ -520,7 +514,7 @@ void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *
     }
 
     // Configure matrix addition kernel
-    if(add_c && !fuse_add)
+    if(add_c && !fuse_add_c)
     {
         _ma_kernel.configure(c, output, beta);
         _run_addition = true;
@@ -539,26 +533,32 @@ Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
     // Select GEMMType
     GEMMType gemm_type = select_gemm_type(m, n, k, a->data_type(), gemm_info.reshape_b_only_on_first_run(), gpu_target);
 
+    const bool is_fuse_add_c_supported = (gemm_type == GEMMType::RESHAPED_V2) || (gemm_type == GEMMType::RESHAPED_ONLY_RHS);
+    const bool add_c                   = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
+    const bool fuse_add_c              = add_c && is_fuse_add_c_supported;
+
+    const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr;
+
     switch(gemm_type)
     {
         case GEMMType::NATIVE:
         {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_native(a, b, c, output, alpha, beta, gemm_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_native(a, b, c_to_use, output, alpha, beta, gemm_info));
             break;
         }
         case GEMMType::RESHAPED_V1:
         {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c, output, alpha, beta, gemm_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
             break;
         }
         case GEMMType::RESHAPED_V2:
         {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v2(a, b, c, output, alpha, beta, gemm_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v2(a, b, c_to_use, output, alpha, beta, gemm_info));
             break;
         }
         case GEMMType::RESHAPED_ONLY_RHS:
         {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c, output, alpha, beta, gemm_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info));
             break;
         }
         default:
@@ -567,6 +567,12 @@ Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
         }
     }
 
+    // Validate matrix addition kernel
+    if(add_c && !fuse_add_c)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAdditionKernel::validate(c, output, beta));
+    }
+
     return Status{};
 }
 
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 4e518fcfd5..99f045a0bf 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -202,8 +202,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     _skip_col2im                = data_layout == DataLayout::NHWC;
     _append_bias                = (biases != nullptr) && (!_is_quantized);
     _is_activationlayer_enabled = act_info.enabled();
-    // In case of F16, fused bias will be used in GEMM
-    _run_addition = (_skip_im2col) && (_append_bias) && (data_type != DataType::F16);
+    _run_addition               = (_skip_im2col) && (_append_bias);
 
     // Set the GPU target for im2col and col2im
     _im2col_kernel.set_target(CLScheduler::get().target());
@@ -388,8 +387,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     const bool skip_im2col                = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
     const bool skip_col2im                = data_layout == DataLayout::NHWC;
     bool       is_activationlayer_enabled = act_info.enabled();
-    // In case of F16, fused bias will be used in GEMM
-    const bool run_addition = (skip_im2col) && (append_bias) && (data_type != DataType::F16);
+    const bool run_addition               = (skip_im2col) && (append_bias);
 
     const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
     const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
diff --git a/tests/framework/Macros.h b/tests/framework/Macros.h
index 591b80e9d8..134f75e287 100644
--- a/tests/framework/Macros.h
+++ b/tests/framework/Macros.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -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, size, ...) size
-#define VARIADIC_SIZE(...) VARIADIC_SIZE_IMPL(__VA_ARGS__, 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, size, ...) size
+#define VARIADIC_SIZE(...) VARIADIC_SIZE_IMPL(__VA_ARGS__, 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, ...) \
@@ -83,6 +83,9 @@
 #define JOIN_PARAM11(OP, param, ...) \
     OP(10, param)                    \
     , JOIN_PARAM10(OP, __VA_ARGS__)
+#define JOIN_PARAM12(OP, param, ...) \
+    OP(11, param)                    \
+    , JOIN_PARAM11(OP, __VA_ARGS__)
 #define JOIN_PARAM(OP, NUM, ...) \
     CONCAT(JOIN_PARAM, NUM)      \
     (OP, __VA_ARGS__)
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
index 564d3f4c2f..69e58303f3 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
@@ -76,6 +76,9 @@ constexpr float         tolerance_num_f16 = 0.02f;
 /** Alpha values to test - Precommit */
 const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} );
 
+/** Beta values to test - Precommit */
+const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} );
+
 /** M values to test */
 const auto m_values = framework::dataset::make("M", 37);
 
@@ -130,8 +133,11 @@ const auto i_values_lhs = framework::dataset::make("interleave_lhs", { true, fal
 /** Interleave values to test with RHS matrix */
 const auto i_values_rhs = framework::dataset::make("interleave_rhs", { true, false });
 
+/** Broadcast bias from vector to matrix */
+const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} );
+
 /** 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, unsigned int v0_value, unsigned int h0_value, bool i_value_lhs, bool i_value_rhs, DataType data_type)
+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, unsigned int v0_value, unsigned int h0_value, bool i_value_lhs, bool i_value_rhs, bool broadcast_bias, DataType data_type)
 {
     const unsigned int M = m_value;
     const unsigned int N = n_value;
@@ -151,7 +157,7 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
     rhs_info.interleave = i_value_rhs;
     rhs_info.transpose  = true;
 
-    GEMMReshapeInfo gemm_info(M, N, K);
+    GEMMReshapeInfo gemm_info(M, N, K, false, false, 0, false, broadcast_bias);
 
     const TensorShape lhs_shape(K, M, b_value);
     const TensorShape lhs_shape_reshaped = compute_lhs_reshaped_shape(TensorInfo(lhs_shape, 1, data_type),
@@ -166,18 +172,24 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
                                                    TensorInfo(rhs_shape_reshaped, 1, data_type),
                                                    gemm_info);
 
+    const TensorShape bias_shape(N,
+                                 broadcast_bias? 1 : M,
+                                 broadcast_bias? 1 : b_value);
+
     // Create tensors
     CLTensor lhs_reshaped = create_tensor<CLTensor>(lhs_shape_reshaped, data_type);
     CLTensor rhs_reshaped = create_tensor<CLTensor>(rhs_shape_reshaped, data_type);
+    CLTensor bias         = create_tensor<CLTensor>(bias_shape, data_type);
     CLTensor dst          = create_tensor<CLTensor>(dst_shape, data_type);
 
     ARM_COMPUTE_EXPECT(lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
     // Create and configure function
     CLGEMMMatrixMultiplyReshaped gemm;
-    gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, 1.0f, lhs_info, rhs_info, gemm_info);
+    gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, gemm_info);
 }
 } // namespace
 
@@ -185,7 +197,7 @@ TEST_SUITE(CL)
 TEST_SUITE(GEMMMatrixMultiplyReshaped)
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
                                                                    n_values),
                                                                    k_values),
@@ -197,13 +209,14 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combi
                                                                    h0_values_precommit),
                                                                    i_values_lhs),
                                                                    i_values_rhs),
-m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, v0_value, h0_value, i_value_lhs, i_value_rhs)
+                                                                   broadcast_bias_values),
+m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, v0_value, h0_value, i_value_lhs, i_value_rhs, broadcast_bias)
 {
-    validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, v0_value, h0_value, i_value_lhs, i_value_rhs, DataType::F32);
+    validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, v0_value, h0_value, i_value_lhs, i_value_rhs, broadcast_bias, DataType::F32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
                                                                    n_values),
                                                                    k_values),
@@ -216,14 +229,16 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<float>, fra
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   broadcast_bias_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture<float>, framework::DatasetMode::NIGHTLY,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
                                                                    n_values),
                                                                    k_values),
@@ -236,14 +251,16 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture<float>, fra
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   broadcast_bias_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_w_values,
                                                                    m_h_values),
                                                                    n_values),
@@ -257,14 +274,15 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>,
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::DatasetMode::NIGHTLY,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_w_values,
                                                                    m_h_values),
                                                                    n_values),
@@ -278,7 +296,8 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>,
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
@@ -287,7 +306,7 @@ TEST_SUITE_END() // FP32
 
 TEST_SUITE(FP16)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
                                                                    n_values),
                                                                    k_values),
@@ -300,14 +319,16 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedFixture<half>, fram
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   broadcast_bias_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture<half>, framework::DatasetMode::NIGHTLY,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_values,
                                                                    n_values),
                                                                    k_values),
@@ -320,14 +341,16 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedFixture<half>, fram
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values),
+                                                                   broadcast_bias_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
 }
 
 FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_w_values,
                                                                    m_h_values),
                                                                    n_values),
@@ -341,14 +364,15 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>,
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>, framework::DatasetMode::NIGHTLY,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
+                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
                                                                    m_w_values,
                                                                    m_h_values),
                                                                    n_values),
@@ -362,7 +386,8 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>,
                                                                    i_values_lhs),
                                                                    i_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values))
+                                                                   a_values),
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index 23ae004912..133170e2d3 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -123,7 +123,7 @@ const auto i_values_rhs = framework::dataset::make("interleave_rhs", { true, fal
 /** Transpose values to test with RHS matrix */
 const auto t_values_rhs = framework::dataset::make("transpose_rhs", { true, false });
 
-/**Broadcast bias from vector to matrix */
+/** Broadcast bias from vector to matrix */
 const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} );
 
 /** Configuration test */
@@ -155,18 +155,15 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
                                                    TensorInfo(rhs_shape_reshaped, 1, data_type),
                                                    gemm_info);
 
+    const TensorShape bias_shape(N,
+                                 broadcast_bias? 1 : M,
+                                 broadcast_bias? 1 : b_value);
+
     // Create tensors
     CLTensor lhs          = create_tensor<CLTensor>(lhs_shape, data_type);
     CLTensor rhs_reshaped = create_tensor<CLTensor>(rhs_shape_reshaped, data_type);
-    CLTensor dst          = create_tensor<CLTensor>(dst_shape, data_type);
-
-    TensorShape bias_shape = dst_shape;
-    if (broadcast_bias)
-    {
-        bias_shape[1] = 1;
-        bias_shape[2] = 1;
-    }
     CLTensor bias         = create_tensor<CLTensor>(bias_shape, data_type);
+    CLTensor dst          = create_tensor<CLTensor>(dst_shape, data_type);
 
     ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -257,7 +254,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture<
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
                                                                    a_values),
-                                                                   b_values))
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
@@ -278,7 +275,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture<
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F32)),
                                                                    a_values),
-                                                                   b_values))
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
@@ -300,7 +297,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture<half
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
                                                                    a_values),
-                                                                   b_values),
+                                                                   beta_values),
                                                                    broadcast_bias_values))
 {
     // Validate output
@@ -321,7 +318,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyReshapedOnlyRHSFixture<half
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
                                                                    a_values),
-                                                                   b_values),
+                                                                   beta_values),
                                                                    broadcast_bias_values))
 {
     // Validate output
@@ -343,7 +340,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture<
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
                                                                    a_values),
-                                                                   b_values))
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
@@ -364,7 +361,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture<
                                                                    t_values_rhs),
                                                                    framework::dataset::make("DataType", DataType::F16)),
                                                                    a_values),
-                                                                   b_values))
+                                                                   beta_values))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index 34f9bd848c..fcb41bb0ba 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -157,7 +157,7 @@ class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture
 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, unsigned int v0, unsigned int h0, bool interleave_lhs,
-               bool interleave_rhs, DataType data_type, float alpha)
+               bool interleave_rhs, DataType data_type, float alpha, float beta, bool broadcast_bias)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0         = m0;
@@ -176,9 +176,12 @@ public:
         // 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);
 
-        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha);
-        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha);
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
+        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
     }
 
 protected:
@@ -193,11 +196,13 @@ protected:
         library->fill_borders_with_garbage(tensor, distribution_inf, i);
     }
 
-    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha)
+    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)
     {
         // Create tensors
-        TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
-        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        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 lhs_reshaped;
         TensorType rhs_reshaped;
         TensorType dst;
@@ -214,20 +219,23 @@ protected:
         GEMMFunctionType       gemm;
         reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
         reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
+        gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, 0, false, broadcast_bias));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
         lhs_reshaped.allocator()->allocate();
         rhs_reshaped.allocator()->allocate();
+        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -235,6 +243,7 @@ protected:
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
 
         // Compute GEMM
         reshape_lhs.run();
@@ -244,7 +253,7 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape[0]          = rhs_shape[0];
@@ -253,13 +262,27 @@ protected:
         // Create reference
         SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
         SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
-        SimpleTensor<T> c{ dst_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_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
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
-        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+        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));
+            }
+        }
+
+        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
     }
 
     TensorType      _target{};
@@ -273,7 +296,7 @@ public:
     template <typename...>
     void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
                bool interleave_lhs,
-               bool interleave_rhs, DataType data_type, float alpha)
+               bool interleave_rhs, DataType data_type, float alpha, float beta)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0         = m0;
@@ -295,9 +318,10 @@ public:
         // 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, 1, 1);
 
-        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha, m_h);
-        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, m_h);
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
+        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
     }
 
 protected:
@@ -308,12 +332,13 @@ protected:
         library->fill(tensor, distribution, i);
     }
 
-    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha,
-                              unsigned int m_h)
+    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, unsigned int m_h)
     {
         // Create tensors
-        TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
-        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        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 lhs_reshaped;
         TensorType rhs_reshaped;
         TensorType dst;
@@ -330,27 +355,31 @@ protected:
         GEMMFunctionType       gemm;
         reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
         reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
+        gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h, false, true));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
         lhs_reshaped.allocator()->allocate();
         rhs_reshaped.allocator()->allocate();
+        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
 
         // Compute GEMM
         reshape_lhs.run();
@@ -360,7 +389,7 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape.set(0, rhs_shape[0]);
@@ -371,13 +400,24 @@ protected:
         // Create reference
         SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
         SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
-        SimpleTensor<T> c{ dst_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_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
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
-        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+        // 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));
+        }
+
+        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
     }
 
     TensorType      _target{};
@@ -406,16 +446,9 @@ public:
         // 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);
-
-        TensorShape bias_shape;
-        if(broadcast_bias)
-        {
-            bias_shape = TensorShape(n, 1, 1);
-        }
-        else
-        {
-            bias_shape = TensorShape(n, m, batch_size);
-        }
+        const TensorShape bias_shape(n,
+                                     broadcast_bias ? 1 : m,
+                                     broadcast_bias ? 1 : batch_size);
 
         _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
         _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
@@ -457,6 +490,7 @@ protected:
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
@@ -468,6 +502,7 @@ protected:
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
@@ -500,20 +535,16 @@ protected:
         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
         if(broadcast_bias)
         {
-            SimpleTensor<T> tmp{ bias_shape, data_type, 1 };
-            fill(tmp, 2);
-            for(int i = 0; i < m * batch_size; i++)
+            // 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, tmp.data(), n * sizeof(T));
+                memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
             }
         }
-        else
-        {
-            fill(bias, 2);
-        }
 
         return (reference::gemm<T>(lhs, rhs, bias, alpha, beta));
     }
@@ -522,27 +553,35 @@ protected:
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
-class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
+class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
 {
 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)
+    void setup(unsigned int m_w, unsigned int m_h, 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, DataType data_type, float alpha, float beta)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0 = m0;
         lhs_info.k0 = k0;
 
         GEMMRHSMatrixInfo rhs_info;
-        rhs_info.n0 = n0;
-        rhs_info.k0 = k0;
+        rhs_info.n0         = n0;
+        rhs_info.k0         = k0;
+        rhs_info.h0         = h0;
+        rhs_info.interleave = interleave_rhs;
+        rhs_info.transpose  = transpose_rhs;
+
+        // In case of GEMM3D, m is the product between m_w and m_h
+        const unsigned int m = m_w * m_h;
 
         // 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, 1, 1);
 
-        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha);
-        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha);
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
+        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
     }
 
 protected:
@@ -551,100 +590,116 @@ protected:
     {
         std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
         library->fill(tensor, distribution, i);
-
-        // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
-        std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), 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 GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha)
+    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,
+                              unsigned int m_h)
     {
         // Create tensors
-        TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
-        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        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;
 
         const unsigned int M = lhs_shape[1];
         const unsigned int N = rhs_shape[0];
         const unsigned int K = lhs_shape[0];
 
+        // The output tensor will be auto-initialized within the function
+
         // Create and configure function
-        GEMMFunctionType gemm;
-        gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
+        ReshapeRHSFunctionType reshape_rhs;
+        GEMMFunctionType       gemm;
+        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
+        gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h, false, true));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
+        rhs_reshaped.allocator()->allocate();
+        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
 
         // Compute GEMM
+        reshape_rhs.run();
         gemm.run();
 
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
     {
         TensorShape dst_shape = lhs_shape;
-        dst_shape[0]          = rhs_shape[0];
-        dst_shape[1]          = lhs_shape[1];
+        dst_shape.set(0, rhs_shape[0]);
+        dst_shape.set(1, lhs_shape[1] / m_h);
+        dst_shape.set(2, m_h);
+        dst_shape.set(3, lhs_shape[2]);
 
         // Create reference
         SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
         SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
-        SimpleTensor<T> c{ dst_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_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
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
-        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+        // 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));
+        }
+
+        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
     }
 
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };
 
-template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
-class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
+template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(unsigned int m_w, unsigned int m_h, 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, DataType data_type, float alpha, float beta)
+    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)
     {
         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;
-
-        // In case of GEMM3D, m is the product between m_w and m_h
-        const unsigned int m = m_w * m_h;
+        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, 1, 1);
 
-        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
-        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
+        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha);
+        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha);
     }
 
 protected:
@@ -653,30 +708,26 @@ protected:
     {
         std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
         library->fill(tensor, distribution, i);
+
+        // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+        std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), 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,
-                              unsigned int m_h)
+    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha)
     {
         // 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 lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
         TensorType dst;
 
         const unsigned int M = lhs_shape[1];
         const unsigned int N = rhs_shape[0];
         const unsigned int K = lhs_shape[0];
 
-        // The output tensor will be auto-initialized within the function
-
         // Create and configure function
-        ReshapeRHSFunctionType reshape_rhs;
-        GEMMFunctionType       gemm;
-        reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
-        gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h, false, true));
+        GEMMFunctionType gemm;
+        gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -684,56 +735,38 @@ protected:
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
-        rhs_reshaped.allocator()->allocate();
-        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
-        fill(AccessorType(bias), 2);
 
         // Compute GEMM
-        reshape_rhs.run();
         gemm.run();
 
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha)
     {
         TensorShape dst_shape = lhs_shape;
-        dst_shape.set(0, rhs_shape[0]);
-        dst_shape.set(1, lhs_shape[1] / m_h);
-        dst_shape.set(2, m_h);
-        dst_shape.set(3, lhs_shape[2]);
+        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 };
-
-        const int n          = rhs_shape[0];
-        const int m          = lhs_shape[1];
-        const int batch_size = lhs_shape[2];
+        SimpleTensor<T> c{ dst_shape, data_type, 1 };
 
         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);
 
-        SimpleTensor<T> tmp{ bias_shape, data_type, 1 };
-        fill(tmp, 2);
-        for(int i = 0; i < m * batch_size; i++)
-        {
-            memcpy(bias.data() + i * n, tmp.data(), n * sizeof(T));
-        }
-
-        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
     }
 
     TensorType      _target{};