diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h
index b96e978..82dcd93 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h
@@ -81,6 +81,9 @@
     const ICLTensor *_input0;
     const ICLTensor *_input1;
     ICLTensor       *_output;
+    bool             _slide_matrix_b;
+    bool             _reinterpret_input_as_3d;
+    bool             _reinterpret_output_as_3d;
 };
 } // namespace arm_compute
 #endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYKERNEL_H__*/
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h
index 12c12ef..690afdb 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -111,5 +111,4 @@
     void run(const Window &window, cl::CommandQueue &queue) override;
 };
 } // namespace arm_compute
-
 #endif /* __ARM_COMPUTE_CLGEMMLOWREDUCTIONKERNEL_H__ */
diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl
index 12ac811..e52f1ea 100644
--- a/src/core/CL/cl_kernels/gemmlowp.cl
+++ b/src/core/CL/cl_kernels/gemmlowp.cl
@@ -40,6 +40,12 @@
  * @note The transposition width step (mult_transpose1xW_width * 4) must be passed at compile time using -DTRANSPOSE1XW_WIDTH_STEP (i.e. -DTRANSPOSE1XW_WIDTH_STEP=2)
  * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
  *
+ * @note In case the output has to be reinterpreted as a 3D tensor (i.e. output of convolution layer), the following information must be passed at compile time:
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -58,13 +64,26 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  cross_plane_pad                    (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_interleaved_transposed_midgard(IMAGE_DECLARATION(src0),
                                                          IMAGE_DECLARATION(src1),
-                                                         IMAGE_DECLARATION(dst))
+                                                         IMAGE_DECLARATION(dst),
+                                                         uint src0_stride_z,
+                                                         uint src1_stride_z,
+                                                         uint dst_stride_z
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                         ,
+                                                         uint cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                        )
 {
-    int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
-    int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
+    const int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int z = get_global_id(2);
 
     // Offset
     const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4;
@@ -75,6 +94,13 @@
     __global uchar *src_addr_a = (__global uchar *)(src0_ptr + y * src0_stride_y + src0_offset_first_element_in_bytes);
     __global uchar *src_addr_b = (__global uchar *)(src1_ptr + x * src1_stride_y + src1_offset_first_element_in_bytes);
 
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr_b += (z % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr_b += z * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     // Compute end row address for matrix B
     __global uchar *src_end_addr_b = src_addr_b + COLS_B;
 
@@ -122,11 +148,49 @@
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D
+    uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store 4x4 block
-    vstore4(c00, 0, (__global int *)(offset(&dst, 0, 0)));
-    vstore4(c10, 0, (__global int *)(offset(&dst, 0, 1)));
-    vstore4(c20, 0, (__global int *)(offset(&dst, 0, 2)));
-    vstore4(c30, 0, (__global int *)(offset(&dst, 0, 3)));
+    vstore4(c00, 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
+    vstore4(c10, 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
+    vstore4(c20, 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
+    vstore4(c30, 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
+
+#else  // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store 4x4 block
+    vstore4(c00, 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+    vstore4(c10, 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+    vstore4(c20, 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+    vstore4(c30, 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 
 /** This OpenCL kernel is optimized for Bifrost and computes the matrix multiplication between matrix A (src0) and matrix B (src1)
@@ -136,6 +200,12 @@
  * @note The transposition width step (mult_transpose1xW_width * 4) must be passed at compile time using -DTRANSPOSE1XW_WIDTH_STEP (i.e. -DTRANSPOSE1XW_WIDTH_STEP=2)
  * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
  *
+ * @note In case the output has to be reinterpreted as a 3D tensor (i.e. output of convolution layer), the following information must be passed at compile time:
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -154,13 +224,26 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  cross_plane_pad                    (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_interleaved_transposed_bifrost(IMAGE_DECLARATION(src0),
                                                          IMAGE_DECLARATION(src1),
-                                                         IMAGE_DECLARATION(dst))
+                                                         IMAGE_DECLARATION(dst),
+                                                         uint src0_stride_z,
+                                                         uint src1_stride_z,
+                                                         uint dst_stride_z
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                         ,
+                                                         uint cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                        )
 {
-    int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
-    int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
+    const int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int z = get_global_id(2);
 
     // Offset
     const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4;
@@ -171,6 +254,13 @@
     __global uchar *src_addr_a = (__global uchar *)(src0_ptr + y * src0_stride_y + src0_offset_first_element_in_bytes);
     __global uchar *src_addr_b = (__global uchar *)(src1_ptr + x * src1_stride_y + src1_offset_first_element_in_bytes);
 
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr_b += (z % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr_b += z * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     // Compute end row address for matrix B
     __global uchar *src_end_addr_b = src_addr_b + COLS_B;
 
@@ -416,11 +506,49 @@
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D
+    uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store 4x4 block
-    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(offset(&dst, 0, 0)));
-    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(offset(&dst, 0, 1)));
-    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(offset(&dst, 0, 2)));
-    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(offset(&dst, 0, 3)));
+    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
+    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
+    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
+    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
+
+#else  // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store 4x4 block
+    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 
 #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
@@ -431,6 +559,12 @@
  * @note The transposition width step (mult_transpose1xW_width * 4) must be passed at compile time using -DTRANSPOSE1XW_WIDTH_STEP (i.e. -DTRANSPOSE1XW_WIDTH_STEP=2)
  * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
  *
+ * @note In case the output has to be reinterpreted as a 3D tensor (i.e. output of convolution layer), the following information must be passed at compile time:
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -449,13 +583,26 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  cross_plane_pad                    (Optional) Bottom paddings in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_interleaved_transposed_bifrost_dot8(IMAGE_DECLARATION(src0),
                                                               IMAGE_DECLARATION(src1),
-                                                              IMAGE_DECLARATION(dst))
+                                                              IMAGE_DECLARATION(dst),
+                                                              uint src0_stride_z,
+                                                              uint src1_stride_z,
+                                                              uint dst_stride_z
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                                              ,
+                                                              uint cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                                             )
 {
-    int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
-    int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int x = get_global_id(0) / TRANSPOSE1XW_WIDTH_STEP;
+    const int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
+    const int z = get_global_id(2);
 
     // Offset
     const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4;
@@ -466,6 +613,13 @@
     __global uchar *src_addr_a = (__global uchar *)(src0_ptr + y * src0_stride_y + src0_offset_first_element_in_bytes);
     __global uchar *src_addr_b = (__global uchar *)(src1_ptr + x * src1_stride_y + src1_offset_first_element_in_bytes);
 
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr_b += (z % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr_b += z * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     // Compute end row address for matrix B
     __global uchar *src_end_addr_b = src_addr_b + COLS_B;
 
@@ -581,11 +735,49 @@
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * 4) by HEIGHT_GEMM3D
+    uint4 zout = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * 4)) / (uint4)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store 4x4 block
-    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(offset(&dst, 0, 0)));
-    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(offset(&dst, 0, 1)));
-    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(offset(&dst, 0, 2)));
-    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(offset(&dst, 0, 3)));
+    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
+    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
+    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
+    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
+
+#else  // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store 4x4 block
+    vstore4((int4)(c00, c01, c02, c03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+    vstore4((int4)(c10, c11, c12, c13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+    vstore4((int4)(c20, c21, c22, c23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+    vstore4((int4)(c30, c31, c32, c33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
 
@@ -599,6 +791,13 @@
  *
  * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A
  *
+ * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time:
+ *       -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -617,10 +816,27 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  src_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the input tensor (only if defined REINTERPRET_INPUT_AS_3D)
+ * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the output tensor (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_midgard(IMAGE_DECLARATION(src0),
                                   IMAGE_DECLARATION(src1),
-                                  IMAGE_DECLARATION(dst))
+                                  IMAGE_DECLARATION(dst),
+                                  uint src0_stride_z,
+                                  uint src1_stride_z,
+                                  uint dst_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                  ,
+                                  uint src_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                  ,
+                                  uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                 )
 {
     int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X;
 
@@ -633,6 +849,47 @@
     // Update address for the matrix B
     src_addr.s1 += idx;
 
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // Since we load a 2D input tile from a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zin) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint4 zin = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D;
+    zin       = min(DEPTH_GEMM3D - 1, zin);
+
+    // Add offset due to the cross plane paddings
+    zin *= (src_cross_plane_pad * src0_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply src0_stride_z by DEPTH_GEMM3D
+    src_addr.s0 += get_global_id(2) * src0_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    src_addr.s0 += get_global_id(2) * src0_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr.s1 += get_global_id(2) * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     int end_row_vec_a = src_addr.s0 + COLS_A;
 
     VECTOR_UINT acc0 = 0;
@@ -725,34 +982,95 @@
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
     }
 
+    const int z = get_global_id(2);
+
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint8 zout = ((uint8)(0, 1, 2, 3, 4, 5, 6, 7) + (uint8)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint8)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (dst_cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store the result
     VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
-    (CONVERT(acc0, VECTOR_INT), 0, (__global int *)(offset(&dst, 0, 0)));
+    (CONVERT(acc0, VECTOR_INT), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
     VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
-    (CONVERT(acc1, VECTOR_INT), 0, (__global int *)(offset(&dst, 0, 1)));
+    (CONVERT(acc1, VECTOR_INT), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
     VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
-    (CONVERT(acc2, VECTOR_INT), 0, (__global int *)(offset(&dst, 0, 2)));
+    (CONVERT(acc2, VECTOR_INT), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
     VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
-    (CONVERT(acc3, VECTOR_INT), 0, (__global int *)(offset(&dst, 0, 3)));
+    (CONVERT(acc3, VECTOR_INT), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
     VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
-    (CONVERT(acc4, VECTOR_INT), 0, (__global int *)(offset(&dst, 0, 4)));
+    (CONVERT(acc4, VECTOR_INT), 0, (__global int *)(dst.ptr + 4 * dst_stride_y + zout.s4));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store the result
+    VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
+    (CONVERT(acc0, VECTOR_INT), 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+    VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
+    (CONVERT(acc1, VECTOR_INT), 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+    VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
+    (CONVERT(acc2, VECTOR_INT), 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+    VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
+    (CONVERT(acc3, VECTOR_INT), 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+    VSTORE(NUM_ELEMS_PROCESSED_PER_THREAD_X)
+    (CONVERT(acc4, VECTOR_INT), 0, (__global int *)(dst.ptr + 4 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 
 /** OpenCL kernel optimized for Bifrost architectures that computes the matrix multiplication between matrix A (src0) and matrix B (src1) in case both matrices have not beed reshaped
  *
  * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A
  *
+ * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time:
+ *       -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -771,10 +1089,27 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  src_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the input tensor (only if defined REINTERPRET_INPUT_AS_3D)
+ * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the output tensor (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_bifrost(IMAGE_DECLARATION(src0),
                                   IMAGE_DECLARATION(src1),
-                                  IMAGE_DECLARATION(dst))
+                                  IMAGE_DECLARATION(dst),
+                                  uint src0_stride_z,
+                                  uint src1_stride_z,
+                                  uint dst_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                  ,
+                                  uint src_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                  ,
+                                  uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+                                 )
 {
     int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X;
 
@@ -787,6 +1122,47 @@
     // Update address for the matrix B
     src_addr.s1 += idx;
 
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // Since we load a 2D input tile from a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zin) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint4 zin = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D;
+    zin       = min(DEPTH_GEMM3D - 1, zin);
+
+    // Add offset due to the cross plane paddings
+    zin *= (src_cross_plane_pad * src0_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply src0_stride_z by DEPTH_GEMM3D
+    src_addr.s0 += get_global_id(2) * src0_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    src_addr.s0 += get_global_id(2) * src0_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr.s1 += get_global_id(2) * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     int end_row_vec_a = src_addr.s0 + COLS_A;
 
     uint acc00 = 0;
@@ -1075,23 +1451,72 @@
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
     }
 
+    const int z = get_global_id(2);
+
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint8 zout = ((uint8)(0, 1, 2, 3, 4, 5, 6, 7) + (uint8)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint8)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (dst_cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store the result
-    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(offset(&dst, 0, 0)));
+    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(offset(&dst, 0, 1)));
+    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(offset(&dst, 0, 2)));
+    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(offset(&dst, 0, 3)));
+    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
-    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(offset(&dst, 0, 4)));
+    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(dst.ptr + 4 * dst_stride_y + zout.s4));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store the result
+    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(dst.ptr + 4 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 
 #if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
@@ -1099,6 +1524,13 @@
  *
  * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A
  *
+ * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time:
+ *       -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D
+ *       -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ *       -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ *       -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ *          (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns matrix A NOT reshaped
+ *
  * @param[in]  src0_ptr                           Pointer to the source matrix. Supported data type: QASYMM8
  * @param[in]  src0_stride_x                      Stride of the source matrix in X dimension (in bytes)
  * @param[in]  src0_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -1117,10 +1549,27 @@
  * @param[in]  dst_stride_y                       Stride of the destination matrix in Y dimension (in bytes)
  * @param[in]  dst_step_y                         dst_gx_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]  src0_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  src1_stride_z                      Stride of the source matrix in Z dimension (in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  src_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the input tensor (only if defined REINTERPRET_INPUT_AS_3D)
+ * @param[in]  dst_cross_plane_pad                (Optional) Bottom paddings in unit of elements for the output tensor (only if defined REINTERPRET_OUTPUT_AS_3D)
  */
 __kernel void gemmlowp_mm_bifrost_dot8(IMAGE_DECLARATION(src0),
                                        IMAGE_DECLARATION(src1),
-                                       IMAGE_DECLARATION(dst))
+                                       IMAGE_DECLARATION(dst),
+                                       uint src0_stride_z,
+                                       uint src1_stride_z,
+                                       uint dst_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+                                       ,
+                                       uint src_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+                                       ,
+                                       uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D)
+                                      )
 {
     int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X;
 
@@ -1133,6 +1582,47 @@
     // Update address for the matrix B
     src_addr.s1 += idx;
 
+#if defined(REINTERPRET_INPUT_AS_3D)
+    // Since we load a 2D input tile from a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zin) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint4 zin = ((uint4)(0, 1, 2, 3) + (uint4)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint4)HEIGHT_GEMM3D;
+    zin       = min(DEPTH_GEMM3D - 1, zin);
+
+    // Add offset due to the cross plane paddings
+    zin *= (src_cross_plane_pad * src0_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply src0_stride_z by DEPTH_GEMM3D
+    src_addr.s0 += get_global_id(2) * src0_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+    // Add offset for batched GEMM
+    src_addr.s0 += get_global_id(2) * src0_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+#if defined(MATRIX_B_DEPTH)
+    // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+    src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z;
+#else  // defined(MATRIX_B_DEPTH)
+    src_addr.s1 += get_global_id(2) * src1_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
     int end_row_vec_a = src_addr.s0 + COLS_A;
 
     uint acc00 = 0;
@@ -1321,23 +1811,72 @@
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
     }
 
+    const int z = get_global_id(2);
+
     // Compute destination address
     Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+    // Since we store a 2D output tile in a 3D tensor, we need to check when the plane changes across the z dimension
+    // in order to take into account the presence of possible cross plane paddings
+    //
+    //  |                  |
+    //  |      plane0      |
+    //  |                  |
+    //  |__________________|
+    //  |******************|
+    //  |  cross_plane_pad |
+    //  |******************|
+    //  |                  |
+    //  |      plane1      |
+    //  |                  |
+    //  |__________________|
+
+    // The plane (zout) is calculated dividing M (get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y) by HEIGHT_GEMM3D
+    uint8 zout = ((uint8)(0, 1, 2, 3, 4, 5, 6, 7) + (uint8)(get_global_id(1) * NUM_ELEMS_PROCESSED_PER_THREAD_Y)) / (uint8)HEIGHT_GEMM3D;
+    zout       = min(DEPTH_GEMM3D - 1, zout);
+
+    // Add offset due to the cross plane paddings
+    zout *= (dst_cross_plane_pad * dst_stride_y);
+
+    // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+    // multiply dst_stride_z by DEPTH_GEMM3D
+    dst.ptr += z * dst_stride_z * DEPTH_GEMM3D;
+
     // Store the result
-    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(offset(&dst, 0, 0)));
+    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y + zout.s0));
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(offset(&dst, 0, 1)));
+    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y + zout.s1));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(offset(&dst, 0, 2)));
+    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y + zout.s2));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(offset(&dst, 0, 3)));
+    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y + zout.s3));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
 #if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
-    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(offset(&dst, 0, 4)));
+    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(dst.ptr + 4 * dst_stride_y + zout.s4));
 #endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+    // Add offset for batched GEMM
+    dst.ptr += z * dst_stride_z;
+
+    // Store the result
+    vstore4((int4)(acc00, acc01, acc02, acc03), 0, (__global int *)(dst.ptr + 0 * dst_stride_y));
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+    vstore4((int4)(acc10, acc11, acc12, acc13), 0, (__global int *)(dst.ptr + 1 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+    vstore4((int4)(acc20, acc21, acc22, acc23), 0, (__global int *)(dst.ptr + 2 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+    vstore4((int4)(acc30, acc31, acc32, acc33), 0, (__global int *)(dst.ptr + 3 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
+#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+    vstore4((int4)(acc40, acc41, acc42, acc43), 0, (__global int *)(dst.ptr + 4 * dst_stride_y));
+#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 4
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
 }
 #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
 
@@ -1480,26 +2019,26 @@
  *                   (sum_row[i] * B_OFFSET) +
  *                   (K_OFFSET)
  *
- * @param[in] mm_result_ptr                                Pointer to the source tensor. Supported data type: S32
- * @param[in] mm_result_stride_x                           Stride of the source tensor in X dimension (in bytes)
- * @param[in] mm_result_step_x                             mm_result_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] mm_result_stride_y                           Stride of the source tensor in Y dimension (in bytes)
- * @param[in] mm_result_step_y                             mm_result_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] mm_result_stride_z                           Stride of the source tensor in Z dimension (in bytes)
- * @param[in] mm_result_step_z                             mm_result_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] mm_result_offset_first_element_in_bytes      The offset of the first element in the source tensor
- * @param[in] sum_col_result_ptr                           Pointer to the source tensor. Supported data type: same as @p mm_result_ptr
- * @param[in] sum_col_result_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in] sum_col_result_step_x                        sum_col_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] sum_col_result_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in] sum_col_result_step_y                        sum_col_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] sum_col_result_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in] sum_row_result_ptr                           Pointer to the source tensor. Supported data type: same as @p mm_result_ptr
- * @param[in] sum_row_result_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in] sum_row_result_step_x                        sum_row_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] sum_row_result_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in] sum_row_result_step_y                        sum_row_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] sum_row_result_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in] mm_result_ptr                           Pointer to the source tensor. Supported data type: S32
+ * @param[in] mm_result_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in] mm_result_step_x                        mm_result_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] mm_result_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] mm_result_step_y                        mm_result_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] mm_result_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] mm_result_step_z                        mm_result_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] mm_result_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in] sum_col_ptr                             Pointer to the source tensor. Supported data type: same as @p mm_result_ptr
+ * @param[in] sum_col_stride_x                        Stride of the source tensor in X dimension (in bytes)
+ * @param[in] sum_col_step_x                          sum_col_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] sum_col_stride_y                        Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] sum_col_step_y                          sum_col_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] sum_col_offset_first_element_in_bytes   The offset of the first element in the source tensor
+ * @param[in] sum_row_ptr                             Pointer to the source tensor. Supported data type: same as @p mm_result_ptr
+ * @param[in] sum_row_stride_x                        Stride of the source tensor in X dimension (in bytes)
+ * @param[in] sum_row_step_x                          sum_row_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] sum_row_stride_y                        Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] sum_row_step_y                          sum_row_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] sum_row_offset_first_element_in_bytes   The offset of the first element in the source tensor
  */
 __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result)
 #if defined(A_OFFSET)
@@ -1514,15 +2053,23 @@
 {
     Tensor3D mm_result = CONVERT_TO_TENSOR3D_STRUCT(mm_result);
 
+    const int y = get_global_id(1);
+    const int z = get_global_id(2);
+
     int4 a_offset_s32 = (int4)0;
     int4 b_offset_s32 = (int4)0;
 
+    int batch_id = z;
+#if defined(DEPTH_INPUT3D)
+    batch_id /= (int)DEPTH_INPUT3D;
+#endif // defined(DEPTH_INPUT3D)
+
 #if defined(A_OFFSET)
     Image sum_col = CONVERT_TO_IMAGE_STRUCT(sum_col);
 
     // Compute the offset contribution due to A_OFFSET
 #if defined(SUM_COL_HAS_BATCHES)
-    a_offset_s32 = vload4(0, (__global int *)(sum_col.ptr + get_global_id(2) * sum_col_stride_y));
+    a_offset_s32 = vload4(0, (__global int *)(sum_col.ptr + batch_id * sum_col_stride_y));
 #else  // defined(MATRIX_B_HAS_BATCHES)
     a_offset_s32 = vload4(0, (__global int *)(sum_col.ptr));
 #endif // defined(MATRIX_B_HAS_BATCHES)
@@ -1534,7 +2081,11 @@
     Image sum_row = CONVERT_TO_IMAGE_STRUCT(sum_row);
 
     // Compute the offset contribution due to B_OFFSET
-    b_offset_s32 = (int4) * (((__global int *)(sum_row.ptr + get_global_id(2) * sum_row_stride_y)) + get_global_id(1));
+#if defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D)
+    b_offset_s32 = (int4) * (((__global int *)(sum_row.ptr + batch_id * sum_row_stride_y)) + (z % (int)DEPTH_INPUT3D) * (int)HEIGHT_INPUT3D + y);
+#else  // defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D)
+    b_offset_s32 = (int4) * (((__global int *)(sum_row.ptr + batch_id * sum_row_stride_y)) + y);
+#endif // defined(HEIGHT_INPUT3D) && defined(DEPTH_INPUT3D)
     b_offset_s32 *= (int4)B_OFFSET;
 #endif // defined(B_OFFSET)
 
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
index 9adf95f..cf66ebd 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
@@ -59,17 +59,12 @@
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D");
 
     if(!is_interleaved_transposed)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
-
-        if(output->total_size() != 0)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
-            ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
-        }
     }
     else
     {
@@ -95,43 +90,82 @@
 
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+    }
 
-        if(output->total_size() != 0)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
-            ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
-        }
+    if(output->total_size() != 0)
+    {
+        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
     }
 
     return Status{};
 }
 
 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, bool is_interleaved_transposed,
-                                                        ElementsProcessed &num_elements_processed)
+                                                        const GEMMReshapeInfo &reshape_info, ElementsProcessed &num_elements_processed)
 {
     unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
     unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+    bool          reinterpret_input_as_3d             = reshape_info.reinterpret_input_as_3d();
+    bool          reinterpret_output_as_3d            = (reshape_info.depth_output_gemm3d() != 1);
 
     Window win{};
+    Window win_out{};
     bool   window_changed = false;
 
+    // In case both input and output have to be reinterpreted as 3D tensors,
+    // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
+    if(reinterpret_input_as_3d == reinterpret_output_as_3d)
+    {
+        reinterpret_input_as_3d  = false;
+        reinterpret_output_as_3d = false;
+    }
+
+    // Output tensor auto inizialitation if not yet initialized
+    auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)));
+
+    TensorInfo tmp_info(*output);
+
+    if(reinterpret_output_as_3d)
+    {
+        // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+        // the window needs to be constructed on the 2D collapsed version of the tensor
+        TensorShape tmp_shape(output->tensor_shape());
+        tmp_shape.collapse(2U, 1U);
+        tmp_info.set_tensor_shape(tmp_shape);
+    }
+
     // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
     if(is_interleaved_transposed)
     {
+        // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
+        ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
+
         // Configure kernel window
         num_elems_processed_per_iteration_x = 4;
         num_elems_processed_per_iteration_y = 4;
 
-        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
+        // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
+        const int m          = reshape_info.m();
+        const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
+
+        win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
         AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
-        AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
-        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+        AccessWindowStatic    input1_access(input1, 0, 0,
+                                            ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),
+                                            ceil_to_multiple(input1->dimension(1), num_elems_processed_per_iteration_y));
+        AccessWindowStatic output_access(output, 0, 0,
+                                         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, output_access);
+        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, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
+        output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
     }
     else
     {
@@ -139,27 +173,42 @@
         num_elems_processed_per_iteration_x = 4;
         num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 5);
 
+        // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
+        // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
+        const int m          = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1];
+        const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
+
         // Configure window
-        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-        AccessWindowStatic    input0_access(input0, 0, 0, input0->dimension(0), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
-        AccessWindowStatic    input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
-        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+        AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad);
+        AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+        AccessWindowStatic output_access(output, 0, 0,
+                                         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, output_access);
+        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
 
         Coordinates coord;
         coord.set_num_dimensions(output->num_dimensions());
         output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
     }
 
+    // Collapse along the Z direction
+    // This collapse needs to be here in order to tune the Z dimension of LWS
+    Window             collapsed             = win;
+    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+    collapsed                                = win.collapse(win, dimension_to_collapse);
+
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
+    return std::make_pair(err, collapsed);
 }
 } // namespace
 
 CLGEMMLowpMatrixMultiplyKernel::CLGEMMLowpMatrixMultiplyKernel()
-    : _input0(nullptr), _input1(nullptr), _output(nullptr)
+    : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false)
 {
 }
 
@@ -176,9 +225,23 @@
 
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));
 
-    _input0 = input0;
-    _input1 = input1;
-    _output = output;
+    _input0                   = input0;
+    _input1                   = input1;
+    _output                   = output;
+    _reinterpret_input_as_3d  = reshape_info.reinterpret_input_as_3d();
+    _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1);
+
+    // In case both input and output have to be reinterpreted as 3D tensors,
+    // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
+    if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
+    {
+        _reinterpret_input_as_3d  = false;
+        _reinterpret_output_as_3d = false;
+    }
+
+    // Check if we need to slide the matrix B
+    const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions();
+    _slide_matrix_b                          = (_input1->info()->num_dimensions() >= num_dimensions_input0);
 
     ElementsProcessed num_elements_processed{};
 
@@ -186,15 +249,21 @@
     GPUTarget arch_target = get_arch_from_target(get_target());
 
     // Configure kernel window
-    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, num_elements_processed);
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, num_elements_processed);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 
     const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
 
     // Create build options
-    CLBuildOptions build_opts;
     std::string    kernel_name(" ");
+    CLBuildOptions build_opts;
+    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");
+    build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
+    build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
+    build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+
     if(is_interleaved_transposed)
     {
         const int mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
@@ -225,6 +294,8 @@
     // Set config_id for enabling LWS tuning
     _config_id = "gemmlowp_";
     _config_id += (is_interleaved_transposed ? "reshaped_" : "");
+    _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()));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
@@ -242,6 +313,7 @@
                                                               input1->clone().get(),
                                                               output->clone().get(),
                                                               is_interleaved_transposed,
+                                                              reshape_info,
                                                               num_elements_processed)
                                 .first);
 
@@ -253,18 +325,33 @@
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    Window slice          = window.first_slice_window_2D();
+    Window slice          = window.first_slice_window_3D();
     Window slice_matrix_b = slice;
-    slice_matrix_b.set(Window::DimX, Window::Dimension(0, _input1->info()->dimension(0), 1));
-    slice_matrix_b.set(Window::DimY, Window::Dimension(0, _input1->info()->dimension(1), 1));
-    slice_matrix_b.set(Window::DimZ, Window::Dimension(0, 1, 1));
+    slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+    slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    if(_reinterpret_input_as_3d)
+    {
+        // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
+        const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 3;
+        const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
+        _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
+    }
+
+    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() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
+        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));
+    }
 
     do
     {
         Window slice_b = slice;
         // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
         // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
-        if(_input1->info()->num_dimensions() < 3)
+        if(_slide_matrix_b)
         {
             slice_b = slice_matrix_b;
         }
@@ -273,7 +360,10 @@
         add_2D_tensor_argument(idx, _input0, slice);
         add_2D_tensor_argument(idx, _input1, slice_b);
         add_2D_tensor_argument(idx, _output, slice);
+        _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]));
+        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
         enqueue(queue, *this, slice, lws_hint());
     }
-    while(window.slide_window_slice_2D(slice));
+    while(window.slide_window_slice_3D(slice));
 }
diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
index aa954ab..3888353 100644
--- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp
@@ -62,16 +62,24 @@
     if(b_offset != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
-        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->dimension(0) != mm_result->dimension(1));
+
+        // Check if input is a 3D reinterpretation
+        const bool reinterpret_as_3d = vector_sum_row != nullptr && mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();
+
+        // Validate input
+        ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
+        ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row != nullptr && vector_sum_row->dimension(0) != mm_result->dimension(1));
 
         TensorShape output_shape = mm_result->tensor_shape();
         if(output_shape.num_dimensions() > 1)
         {
+            const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
+
             TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
             vector_sum_row_shape.collapse_from(1);
-            output_shape.collapse_from(2);
+            output_shape.collapse_from(output_batch_idx);
 
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[2],
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
                                             "mm_result tensor must have the same number of batches of output tensor");
 
             if(a_offset != 0)
@@ -98,20 +106,17 @@
     Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration));
 
     AccessWindowHorizontal mm_result_access(mm_result, 0, num_elems_processed_per_iteration);
-    window_changed = window_changed || update_window_and_padding(win,
-                                                                 mm_result_access);
+    window_changed = window_changed || update_window_and_padding(win, mm_result_access);
 
     if(a_offset != 0)
     {
         AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration);
-        window_changed = window_changed || update_window_and_padding(win,
-                                                                     vector_sum_col_access);
+        window_changed = window_changed || update_window_and_padding(win, vector_sum_col_access);
     }
     if(b_offset != 0)
     {
         AccessWindowStatic vector_sum_row_access(vector_sum_row, 0, 0, vector_sum_row->dimension(0), 0); // NOLINT
-        window_changed = window_changed || update_window_and_padding(win,
-                                                                     vector_sum_row_access);
+        window_changed = window_changed || update_window_and_padding(win, vector_sum_row_access);
     }
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
@@ -137,6 +142,11 @@
     _vector_sum_row = vector_sum_row;
     _mm_result      = mm_result;
 
+    // Check if input is a 3D reinterpretation
+    const bool reinterpret_as_3d = vector_sum_row != nullptr
+                                   && mm_result->info()->num_dimensions() > 1
+                                   && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();
+
     // Set the arguments to pass at compile time
     CLBuildOptions build_opts;
 
@@ -149,6 +159,8 @@
     // If b_offset == 0, vector_sum_row can be a nullptr
     build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
     build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * k));
+    build_opts.add_option_if(reinterpret_as_3d, "-DHEIGHT_INPUT3D=" + support::cpp11::to_string(mm_result->info()->dimension(1)));
+    build_opts.add_option_if(reinterpret_as_3d, "-DDEPTH_INPUT3D=" + support::cpp11::to_string(mm_result->info()->dimension(2)));
 
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_offset_contribution", build_opts.options()));
@@ -194,11 +206,13 @@
     // Set window for vector_sum_col
     Window win_vector_sum_col = slice;
     win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
+    win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
     // Set window for vector_sum_row
     Window win_vector_sum_row = slice;
     win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
     win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
+    win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
     do
     {
diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
index cd26cd1..9cf5d1f 100644
--- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
@@ -196,8 +196,8 @@
     Window slice_out = collapsed.first_slice_window_2D();
     Window slice_in  = slice_out;
 
-    slice_in.set(Window::DimY, Window::Dimension(0, 1, 1));
-    slice_in.set(Window::DimZ, Window::Dimension(0, 1, 1));
+    slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+    slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
     do
     {
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 9dbfd3e..821464e 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -72,8 +72,18 @@
 } // namespace
 
 CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _original_b(nullptr), _is_interleaved_transposed(false),
-      _run_addition(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
+    : _memory_group(std::move(memory_manager)),
+      _interleave_kernel(),
+      _transpose_kernel(),
+      _mm_kernel(),
+      _ma_kernel(),
+      _tmp_a(),
+      _tmp_b(),
+      _original_b(nullptr),
+      _is_interleaved_transposed(false),
+      _run_addition(false),
+      _reshape_b_only_on_first_run(false),
+      _is_prepared(false)
 {
 }
 
@@ -146,8 +156,9 @@
     }
 
     // Configure and tune matrix multiply kernel
-    _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d,
-                                                                                                        reinterpret_input_as_3d));
+    _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k,
+                                                                                                        mult_transpose1xW_width, mult_interleave4x4_height,
+                                                                                                        depth_output_gemm3d, reinterpret_input_as_3d));
     CLScheduler::get().tune_kernel_static(_mm_kernel);
 
     if(_is_interleaved_transposed)
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index c9daea4..bd5e969 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -130,9 +130,10 @@
 {
     const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
 
-    const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
     if(is_quantized)
     {
+        const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
+
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
         // Extract and negate input and weights offset
         const QuantizationInfo input_quantization_info   = input->quantization_info();
@@ -148,6 +149,8 @@
     }
     else
     {
+        const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
+
         // Perform validation step on Matrix multiply function
         return CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
     }
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 763ebce..1d6f343 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -107,15 +107,23 @@
     // Arguments used by GEMMReshapeInfo
     // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
     // in order to know how the matrices have been reshaped
+    bool          reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
     const int     m                         = a->info()->dimension(1);
     const int     n                         = b->info()->dimension(0);
     const int     k                         = a->info()->dimension(0);
+    const int     depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
     constexpr int mult_transpose1xW_width   = 1;
     constexpr int mult_interleave4x4_height = 1;
 
     // Check if we need to reshape the matrix A and matrix B
     _is_interleaved_transposed = is_interleaved_transposed(m, n, k, _reshape_b_only_on_first_run, gpu_target);
 
+    // if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
+    if(_is_interleaved_transposed)
+    {
+        reinterpret_input_as_3d = false;
+    }
+
     if(_is_interleaved_transposed)
     {
         matrix_a = &_tmp_a;
@@ -128,14 +136,15 @@
         }
 
         // Configure interleave kernel
-        _mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height);
+        _mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d());
 
         // Configure transpose kernel
         _mtx_b_reshape_kernel.configure(b, &_tmp_b, mult_transpose1xW_width);
     }
-
     // Configure matrix multiply kernel
-    _mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));
+    _mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k,
+                                                                                                 mult_transpose1xW_width, mult_interleave4x4_height,
+                                                                                                 depth_output_gemm3d, reinterpret_input_as_3d));
 
     // Initialize matrix B reduction kernel only if _a_offset is not equal to 0
     if(_a_offset != 0)
@@ -191,28 +200,30 @@
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
-                                    "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(1) != (output)->dimension(1),
-                                    "The output matrix must have the same number of rows as the matrix A");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0),
-                                    "The output matrix must have the same number of columns as the matrix B");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
 
     int32_t a_offset = a->quantization_info().offset;
     int32_t b_offset = b->quantization_info().offset;
 
-    const int             m                         = a->dimension(1);
-    const int             n                         = b->dimension(0);
-    const int             k                         = a->dimension(0);
-    constexpr int         mult_transpose1xW_width   = 1;
-    constexpr int         mult_interleave4x4_height = 1;
-    const int             depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
-    const GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d);
+    const int     m                         = a->dimension(1);
+    const int     n                         = b->dimension(0);
+    const int     k                         = a->dimension(0);
+    constexpr int mult_transpose1xW_width   = 1;
+    constexpr int mult_interleave4x4_height = 1;
+    bool          reinterpret_input_as_3d   = gemm_info.reinterpret_input_as_3d();
+    const int     depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
 
     bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target());
 
+    // if reshape_matrices is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D
+    if(reshape_matrices)
+    {
+        reinterpret_input_as_3d = false;
+    }
+
+    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d);
+
     if(reshape_matrices)
     {
         TensorInfo info_a(compute_interleaved_shape(*a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d()), 1, a->data_type());
diff --git a/tests/datasets/LargeGEMMLowpDataset.h b/tests/datasets/LargeGEMMLowpDataset.h
index 5c0230e..65cb742 100644
--- a/tests/datasets/LargeGEMMLowpDataset.h
+++ b/tests/datasets/LargeGEMMLowpDataset.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -52,6 +52,26 @@
         add_config(TensorShape(941U, 1011U), TensorShape(623U, 941U), TensorShape(623U, 1011U), -9, 1);
     }
 };
+class LargeGEMMLowpOutput3DDataset final : public GEMMLowpDataset
+{
+public:
+    LargeGEMMLowpOutput3DDataset()
+    {
+        add_config(TensorShape(923U, 429U), TensorShape(871U, 923U), TensorShape(871U, 143U, 3U), 0, 0);
+        add_config(TensorShape(681U, 1025U), TensorShape(213U, 681U), TensorShape(213U, 205U, 5U), -3, 2);
+        add_config(TensorShape(364U, 3025U), TensorShape(96U, 364U), TensorShape(96U, 605U, 5U), 2, 3);
+    }
+};
+class LargeGEMMLowpInputOutput3DDataset final : public GEMMLowpDataset
+{
+public:
+    LargeGEMMLowpInputOutput3DDataset()
+    {
+        add_config(TensorShape(923U, 143U, 3U), TensorShape(871U, 923U), TensorShape(871U, 143U, 3U), 0, 0);
+        add_config(TensorShape(681U, 205U, 5U), TensorShape(213U, 681U), TensorShape(213U, 205U, 5U), -2, 5);
+        add_config(TensorShape(364U, 605U, 5U), TensorShape(96U, 364U), TensorShape(96U, 605U, 5U), 2, 4);
+    }
+};
 } // namespace datasets
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/datasets/SmallGEMMLowpDataset.h b/tests/datasets/SmallGEMMLowpDataset.h
index b6651bd..40f0c71 100644
--- a/tests/datasets/SmallGEMMLowpDataset.h
+++ b/tests/datasets/SmallGEMMLowpDataset.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -52,6 +52,32 @@
         add_config(TensorShape(32U, 72U), TensorShape(17U, 32U), TensorShape(17U, 72U), -9, 1);
     }
 };
+class SmallGEMMLowpOutput3DDataset final : public GEMMLowpDataset
+{
+public:
+    SmallGEMMLowpOutput3DDataset()
+    {
+        add_config(TensorShape(21U, 14U), TensorShape(34U, 21U), TensorShape(34U, 7U, 2U), 0, 0);
+        add_config(TensorShape(31U, 1U), TensorShape(23U, 31U), TensorShape(23U, 1U, 1U), -2, 13);
+        add_config(TensorShape(38U, 12U), TensorShape(21U, 38U), TensorShape(21U, 4U, 3U), 0, 4);
+        add_config(TensorShape(32U, 1U), TensorShape(17U, 32U), TensorShape(17U, 1U, 1U), -2, 1);
+        add_config(TensorShape(16U, 16U), TensorShape(8U, 16U), TensorShape(8U, 8U, 2U), 5, 9);
+        add_config(TensorShape(16U, 16U, 5U), TensorShape(8U, 16U, 5U), TensorShape(8U, 8U, 2U, 5U), -7, 2);
+    }
+};
+class SmallGEMMLowpInputOutput3DDataset final : public GEMMLowpDataset
+{
+public:
+    SmallGEMMLowpInputOutput3DDataset()
+    {
+        add_config(TensorShape(21U, 14U, 13U), TensorShape(34U, 21U), TensorShape(34U, 14U, 13U), 0, 0);
+        add_config(TensorShape(31U, 1U, 3U), TensorShape(23U, 31U), TensorShape(23U, 1U, 3U), 0, 0);
+        add_config(TensorShape(38U, 12U, 2U), TensorShape(21U, 38U), TensorShape(21U, 12U, 2U), -2, 13);
+        add_config(TensorShape(32U, 1U, 4U, 3U), TensorShape(17U, 32U), TensorShape(17U, 1U, 4U, 3U), 0, 4);
+        add_config(TensorShape(16U, 16U, 3U, 2U), TensorShape(8U, 16U), TensorShape(8U, 16U, 3U, 2U), -2, 0);
+        add_config(TensorShape(16U, 16U, 5U, 3U), TensorShape(8U, 16U), TensorShape(8U, 16U, 5U, 3U), -9, 1);
+    }
+};
 } // namespace datasets
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/validate_examples/cl_gemm.cpp b/tests/validate_examples/cl_gemm.cpp
index e0aefbf..cdaa33f 100644
--- a/tests/validate_examples/cl_gemm.cpp
+++ b/tests/validate_examples/cl_gemm.cpp
@@ -285,7 +285,7 @@
                 fill(ref_src0, 0);
                 fill(ref_src1, 1);
 
-                SimpleTensor<int32_t> ref_tmp_dst = reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(ref_src0, ref_src1, offset_src0, offset_src1);
+                SimpleTensor<int32_t> ref_tmp_dst = reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(ref_src0, ref_src1, TensorShape(N, M), offset_src0, offset_src1);
 
                 if(add_bias)
                 {
diff --git a/tests/validation/CL/GEMMLowp.cpp b/tests/validation/CL/GEMMLowp.cpp
index 5148a31..42bb212 100644
--- a/tests/validation/CL/GEMMLowp.cpp
+++ b/tests/validation/CL/GEMMLowp.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -47,6 +47,7 @@
 TEST_SUITE(GEMMLowp)
 
 TEST_SUITE(MatrixMultiplyCore)
+
 using CLGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyCore>;
 
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()),
@@ -81,6 +82,33 @@
     validate(CLAccessor(_target), _reference);
 }
 
+TEST_SUITE(Output3D)
+using CLGEMMLowpMatrixMultiplyCoreOutput3DFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyCore, false, true>;
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyCoreOutput3DFixture, framework::DatasetMode::PRECOMMIT, datasets::SmallGEMMLowpOutput3DDataset())
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMLowpMatrixMultiplyCoreOutput3DFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpOutput3DDataset())
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END() // Output3D
+
+TEST_SUITE(InputOutput3D)
+using CLGEMMLowpMatrixMultiplyCoreInputOutput3DFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyCore, true, true>;
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyCoreInputOutput3DFixture, framework::DatasetMode::PRECOMMIT, datasets::SmallGEMMLowpInputOutput3DDataset())
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMLowpMatrixMultiplyCoreInputOutput3DFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpInputOutput3DDataset())
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END() // InputOutput3D
 TEST_SUITE_END() // MatrixMultiplyCore
 
 TEST_SUITE(OutputStage)
diff --git a/tests/validation/fixtures/GEMMLowpAssemblyFixture.h b/tests/validation/fixtures/GEMMLowpAssemblyFixture.h
index d6b94a1..519932f 100644
--- a/tests/validation/fixtures/GEMMLowpAssemblyFixture.h
+++ b/tests/validation/fixtures/GEMMLowpAssemblyFixture.h
@@ -128,7 +128,7 @@
             fill(b, 1, 0, 255);
         }
 
-        return reference::gemmlowp<int32_t, T2>(a, b);
+        return reference::gemmlowp<int32_t, T2>(a, b, shape_c);
     }
 
     TensorType            _target{};
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 06d6be3..73cb832 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -42,7 +42,7 @@
 {
 namespace validation
 {
-template <typename TensorType, typename AccessorType, typename FunctionType>
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
 class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
 {
 public:
@@ -62,8 +62,7 @@
         library->fill(tensor, distribution, i);
     }
 
-    TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c,
-                              int32_t a_offset, int32_t b_offset)
+    TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
     {
         // Create tensors
         TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
@@ -74,8 +73,9 @@
         b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
 
         // Create and configure function
+        // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
         FunctionType gemmlowp;
-        gemmlowp.configure(&a, &b, &c);
+        gemmlowp.configure(&a, &b, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 1), reinterpret_input_as_3d));
 
         ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -99,18 +99,24 @@
         return c;
     }
 
-    SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c,
-                                            int32_t a_offset, int32_t b_offset)
+    SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
     {
+        TensorShape shape_a_to_use = shape_a;
+        if(reinterpret_input_as_3d)
+        {
+            // Collapse the second and third dimension if the input is 3D
+            shape_a_to_use.collapse(2U, 1U);
+        }
+
         // Create reference
-        SimpleTensor<uint8_t> a{ shape_a, DataType::QASYMM8, 1 };
+        SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
         SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
 
         // Fill reference
         fill(a, 0);
         fill(b, 1);
 
-        return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, a_offset, b_offset);
+        return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
     }
 
     TensorType            _target{};
diff --git a/tests/validation/reference/GEMMLowp.cpp b/tests/validation/reference/GEMMLowp.cpp
index 8e41aef..9a7e409 100644
--- a/tests/validation/reference/GEMMLowp.cpp
+++ b/tests/validation/reference/GEMMLowp.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -98,41 +98,52 @@
 } // namespace
 
 template <typename T_out, typename T_in>
-SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in> &b, int32_t a_offset, int32_t b_offset)
+SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
 {
     static_assert(std::is_same<typename std::decay<T_out>::type, int32_t>::value, "Only int32_t is allowed for the output");
 
-    TensorShape         shape(b.shape()[0], a.shape()[1]);
     DataType            dt = std::is_same<T_out, int32_t>::value ? DataType::S32 : DataType::U32;
-    SimpleTensor<T_out> c(shape, dt);
+    SimpleTensor<T_out> c(shape_c, dt);
 
-    const int K       = a.shape().x();
-    const int b_width = b.shape().x();
-    const int rows    = c.shape().y(); //M
-    const int cols    = c.shape().x(); //N
+    const int K = a.shape().x();
+    const int M = a.shape().y();
+    const int N = b.shape().x();
+    const int D = a.shape().z(); // Number of matrices in a batch
+
+    const int a_stride_z = K * M;
+    // Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions
+    const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0;
+    const int c_stride_z = N * M;
 
     std::vector<T_out> acc;
-    acc.resize(cols);
+    acc.resize(N);
 
-    for(int i = 0; i < rows; ++i)
+    for(int depth = 0; depth < D; ++depth)
     {
-        for(int j = 0; j < cols; ++j)
+        const int base_addr_a = depth * a_stride_z;
+        const int base_addr_b = depth * b_stride_z;
+        const int base_addr_c = depth * c_stride_z;
+
+        for(int i = 0; i < M; ++i)
         {
-            acc[j] = 0;
-        }
-        for(int k = 0; k < K; ++k)
-        {
-            const T_out tmp_a = a_offset + static_cast<T_out>(a[k + i * K]);
-            for(int j = 0; j < b_width; ++j)
+            for(int j = 0; j < N; ++j)
             {
-                const T_out tmp_b       = b_offset + static_cast<T_out>(b[j + k * b_width]);
-                const T_out mult_as_int = tmp_a * tmp_b;
-                acc[j] += mult_as_int;
+                acc[j] = 0;
             }
-        }
-        for(int j = 0; j < cols; ++j)
-        {
-            c[j + i * cols] = acc[j];
+            for(int k = 0; k < K; ++k)
+            {
+                const T_out tmp_a = a_offset + static_cast<T_out>(a[base_addr_a + k + i * K]);
+                for(int j = 0; j < N; ++j)
+                {
+                    const T_out tmp_b       = b_offset + static_cast<T_out>(b[base_addr_b + j + k * N]);
+                    const T_out mult_as_int = tmp_a * tmp_b;
+                    acc[j] += mult_as_int;
+                }
+            }
+            for(int j = 0; j < N; ++j)
+            {
+                c[base_addr_c + j + i * N] = acc[j];
+            }
         }
     }
 
@@ -141,9 +152,9 @@
 
 // used to validate assembly kernels which don't know anything about offsets
 template <typename T1, typename T2>
-SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b)
+SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b, TensorShape shape_c)
 {
-    return gemmlowp_matrix_multiply_core<T1, T2>(a, b, 0, 0);
+    return gemmlowp_matrix_multiply_core<T1, T2>(a, b, shape_c, 0, 0);
 }
 
 template <typename T>
@@ -198,10 +209,10 @@
                                                                            int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, int32_t result_mult_int,
                                                                            int32_t result_shift, int32_t min, int32_t max);
-template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, int32_t a_offset, int32_t b_offset);
-template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, int32_t a_offset, int32_t b_offset);
-template SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b);
-template SimpleTensor<int32_t> gemmlowp(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b);
+template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
+template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
+template SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
+template SimpleTensor<int32_t> gemmlowp(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c);
 } // namespace reference
 } // namespace validation
 } // namespace test
diff --git a/tests/validation/reference/GEMMLowp.h b/tests/validation/reference/GEMMLowp.h
index a3d0beb..4396155 100644
--- a/tests/validation/reference/GEMMLowp.h
+++ b/tests/validation/reference/GEMMLowp.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -38,13 +38,13 @@
 template <typename T>
 SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min = 0, int32_t max = 0);
 template <typename T1, typename T2>
-SimpleTensor<T1> gemmlowp_matrix_multiply_core(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b, int32_t a_offset, int32_t b_offset);
+SimpleTensor<T1> gemmlowp_matrix_multiply_core(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
 
 template <typename T>
 SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift);
 
 template <typename T1, typename T2>
-SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b);
+SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T2> &b, TensorShape shape_c);
 
 template <typename T>
 SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, int32_t result_mult_int, int32_t result_shift,