Gemm changes to enable fp16 in armv8a multi_isa builds

        * Code guarded with __ARM_FEATURE_FP16_VECTOR_ARITHMETIC needs
          to be moved to an fp16.cpp file to allow compilation with
          -march=armv8.2-a+fp16

        * fp16.cpp needs to use the templates vector_matrix_multiply_f16() and
          matrix_matrix_multiply_f16 which had to be moved from impl.cpp to fp16.cpp

        * Partially resolves MLCE-1102

Change-Id: Ic87440797d6f1653c815ab6565972206f5afd0ad
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10345
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

6 files changed, 425 insertions, 436 deletions

diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp b/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
index 2d61b72078..505a37174e 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -25,10 +25,55 @@
 
 #include "src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h"
 
+#include <arm_neon.h>
+
 namespace arm_compute
 {
 namespace cpu
 {
+namespace
+{
+void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+    const float16x8_t beta_f16 = vdupq_n_f16(beta);
+
+    constexpr int window_step_x  = 16;
+    const auto    window_start_x = static_cast<int>(window.x().start());
+    const auto    window_end_x   = static_cast<int>(window.x().end());
+
+    Window win = window.collapse_if_possible(window, Window::DimZ);
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(src, win);
+    Iterator out(dst, win);
+
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        const auto in_ptr  = reinterpret_cast<const float16_t *>(in.ptr());
+        const auto out_ptr = reinterpret_cast<float16_t *>(out.ptr());
+
+        int x = window_start_x;
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
+        {
+            float16x8x2_t       alpha_ab = vld2q_f16(out_ptr + x);
+            const float16x8x2_t c        = vld2q_f16(in_ptr + x);
+            // Multiply matrix C by its weight and accumulate
+            alpha_ab.val[0] = vaddq_f16(alpha_ab.val[0], vmulq_f16(c.val[0], beta_f16));
+            alpha_ab.val[1] = vaddq_f16(alpha_ab.val[1], vmulq_f16(c.val[1], beta_f16));
+
+            vst2q_f16(out_ptr + x, alpha_ab);
+        }
+
+        // Left-over loop
+        for(; x < window_end_x; ++x)
+        {
+            *(out_ptr + x) += *(in_ptr + x) * static_cast<float16_t>(beta);
+        }
+    },
+    in, out);
+}
+} // namespace
 void neon_fp16_gemm_matrix_add(const ITensor *src, ITensor *dst, const Window &window, float beta)
 {
     return matrix_addition_f16(src, dst, window, beta);
diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
index 675ed1bbcb..dd0384ca13 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2022 Arm Limited.
+ * Copyright (c) 2016-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -72,48 +72,5 @@ void matrix_addition_f32(const ITensor *src, ITensor *dst, const Window &window,
     },
     in, out);
 }
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
-    const float16x8_t beta_f16 = vdupq_n_f16(beta);
-
-    constexpr int window_step_x  = 16;
-    const auto    window_start_x = static_cast<int>(window.x().start());
-    const auto    window_end_x   = static_cast<int>(window.x().end());
-
-    Window win = window.collapse_if_possible(window, Window::DimZ);
-    win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
-    Iterator in(src, win);
-    Iterator out(dst, win);
-
-    execute_window_loop(win, [&](const Coordinates &)
-    {
-        const auto in_ptr  = reinterpret_cast<const float16_t *>(in.ptr());
-        const auto out_ptr = reinterpret_cast<float16_t *>(out.ptr());
-
-        int x = window_start_x;
-        for(; x < (window_end_x - window_step_x); x += window_step_x)
-        {
-            float16x8x2_t       alpha_ab = vld2q_f16(out_ptr + x);
-            const float16x8x2_t c        = vld2q_f16(in_ptr + x);
-            // Multiply matrix C by its weight and accumulate
-            alpha_ab.val[0] = vaddq_f16(alpha_ab.val[0], vmulq_f16(c.val[0], beta_f16));
-            alpha_ab.val[1] = vaddq_f16(alpha_ab.val[1], vmulq_f16(c.val[1], beta_f16));
-
-            vst2q_f16(out_ptr + x, alpha_ab);
-        }
-
-        // Left-over loop
-        for(; x < window_end_x; ++x)
-        {
-            *(out_ptr + x) += *(in_ptr + x) * static_cast<float16_t>(beta);
-        }
-    },
-    in, out);
-}
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
 } // namespace cpu
 } // namespace arm_compute
diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
index ff35f28b11..26ac99b483 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -30,10 +30,6 @@ namespace arm_compute
 {
 namespace cpu
 {
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
 void matrix_addition_f32(const ITensor *src, ITensor *dst, const Window &window, float beta);
 
 } // namespace cpu
diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp b/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp
index 1bd5a57fab..fae26a5dd6 100644
--- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp
+++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,11 +24,385 @@
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 
 #include "src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h"
+#include "src/core/utils/helpers/float_ops.h"
+
+#include <arm_neon.h>
 
 namespace arm_compute
 {
 namespace cpu
 {
+void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    const auto width_matrix_b  = static_cast<int>(dst->info()->dimension(0));
+    const auto in_b_stride     = static_cast<int>(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size());
+    const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
+
+    // The implementation computes 32 elements per iteration
+    const int window_start_x = 32 * info.thread_id;
+    const int window_step_x  = 32 * info.num_threads;
+    const int window_end_x   = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
+    ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x");
+
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_b;
+    // 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(rhs->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator ina(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, win_out);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
+
+    execute_window_loop(win_out, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        // Here we don't check for x lower equal than (window_end_x - window_step_x) because of
+        // window_end_x is computed above which may cause out-of-bound writes to the dst.
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
+        {
+            if(x > width_matrix_b)
+            {
+                return;
+            }
+
+            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
+
+            float16x8_t acc0 = vdupq_n_f16(0.f);
+            float16x8_t acc1 = vdupq_n_f16(0.f);
+            float16x8_t acc2 = vdupq_n_f16(0.f);
+            float16x8_t acc3 = vdupq_n_f16(0.f);
+
+            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
+            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
+            for(; vec_a <= (vec_a_end_addr - 4);)
+            {
+                const float16x4_t a0l = vld1_f16(vec_a);
+
+                float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+                float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
+                float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
+                float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
+                float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0));
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1));
+
+                matrix_b += 2 * in_b_stride;
+
+                b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+                b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
+                b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
+                b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
+                b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2));
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3));
+
+                vec_a += 4;
+                matrix_b += 2 * in_b_stride;
+            }
+
+            for(; vec_a < vec_a_end_addr; ++vec_a)
+            {
+                const float16_t   a0  = *vec_a;
+                const float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0));
+                acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0));
+                acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0));
+                acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0));
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_alpha)
+            {
+                acc0 = vmulq_f16(acc0, alpha_f16);
+                acc1 = vmulq_f16(acc1, alpha_f16);
+                acc2 = vmulq_f16(acc2, alpha_f16);
+                acc3 = vmulq_f16(acc3, alpha_f16);
+            }
+
+            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
+
+            vst1q_f16(vec_out + 0, acc0);
+            vst1q_f16(vec_out + 8, acc1);
+            vst1q_f16(vec_out + 16, acc2);
+            vst1q_f16(vec_out + 24, acc3);
+        }
+
+        for(; x < window_end_x; ++x)
+        {
+            if(x > width_matrix_b)
+            {
+                return;
+            }
+
+            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
+
+            float16x4_t vacc = vdup_n_f16(0.f);
+
+            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
+            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
+            for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
+            {
+                const float16x4_t a0l = vld1_f16(vec_a);
+
+                const float16x4_t b_col =
+                {
+                    *(matrix_b + 0 * in_b_stride),
+                    *(matrix_b + 1 * in_b_stride),
+                    *(matrix_b + 2 * in_b_stride),
+                    *(matrix_b + 3 * in_b_stride),
+                };
+
+                vacc = vadd_f16(vacc, vmul_f16(a0l, b_col));
+
+                matrix_b += 4 * in_b_stride;
+            }
+
+            float16_t acc = vget_lane_f16(vacc, 0) + vget_lane_f16(vacc, 1) + vget_lane_f16(vacc, 2) + vget_lane_f16(vacc, 3);
+
+            for(; vec_a < vec_a_end_addr; ++vec_a)
+            {
+                const float16_t a0  = *vec_a;
+                const float16_t b00 = *matrix_b;
+
+                acc += b00 * a0;
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_alpha)
+            {
+                acc *= static_cast<float16_t>(alpha);
+            }
+
+            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
+
+            *(vec_out) = acc;
+        }
+    },
+    ina, inb, out);
+}
+
+void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    ARM_COMPUTE_UNUSED(info);
+    const int    out_width            = static_cast<int>(dst->info()->dimension(0));
+    const int    out_height           = static_cast<int>(dst->info()->dimension(1));
+    const size_t in_b_stride          = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
+    const size_t out_stride           = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->info()->data_type());
+    const int    num_elems_matrix_b_x = rhs->info()->dimension(0);
+
+    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the dst matrix
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
+
+    Window win_b;
+    // 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(rhs->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    // Set step_x and step_y for matrix B. Scale by a factor of 8 the X range as the input transposed matrix A has 8 times less the cols of the dst matrix
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator ina(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, window);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto   *mtx_a0  = reinterpret_cast<const float16_t *>(ina.ptr());
+        const auto   *mtx_b0  = reinterpret_cast<const float16_t *>(inb.ptr());
+        auto         *mtx_out = reinterpret_cast<float16_t *>(out.ptr());
+        float16x8x4_t c =
+        {
+            {
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f)
+            }
+        };
+
+        /*
+        This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
+             |a00 a01 a02 a03 | a04 a05 a06 a07|
+             |a10 a11 a12 a13 | a14 a15 a16 a17|
+             |a20 a21 a22 a23 | a24 a25 a26 a27| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | a40 a50 a60 a70 | ...
+             |a30 a31 a32 a33 | a34 a35 a36 a37|   | a04 a14 a24 a34 || a05 a15 a25 a35 || a06 a15 a26 a36 || a07 a17 a27 a37 | a44 a54 a64 a74 | ...
+             |a40 a41 a42 a43 | a44 a45 a46 a47|
+             |a50 a51 a52 a53 | a54 a55 a56 a57|
+             |a60 a61 a62 a63 | a64 a65 a66 a67|
+             |a70 a71 a72 a73 | a74 a75 a76 a77|
+
+             After this operation, the dst matrix will have the following shape: [ height * 4, width / 4 ]
+
+        B Matrix has been transposed as shown below
+
+           |b00 b01 b02 b03 b04 b05 b06 b07|
+           |b10 b11 b12 b13 b14 b15 b16 b17|
+           |b20 b21 b22 b23 b24 b25 b26 b27|
+           |b30 b31 b32 b33 b34 b35 b36 b37|
+          ------------------->
+
+           |b00 b01 b02 b03 b04 b05 b06 b07||b10 b11 b12 b13 b14 b15 b16 b17||b20 b21 b22 b23 b24 b25 b26 b27||b30 b31 b32 b33 b34 b35 b36 b37|
+
+            c.val[0][0] = a00*b00 + a01*b10 + a02*b20 + a03*b30
+            c.val[0][1] = a00*b01 + a01*b11 + a02*b21 + a03*b31
+
+        The size of the dst tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
+        */
+        const float16_t *mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
+
+        for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
+
+        {
+            const float16x8_t p00 = vld1q_f16(mtx_a0);
+            const float16x8_t p02 = vld1q_f16(mtx_a0 + 8);
+
+            const float16x8_t q00 = vld1q_f16(mtx_b0);
+            const float16x8_t q02 = vld1q_f16(mtx_b0 + 8);
+            const float16x8_t q04 = vld1q_f16(mtx_b0 + 16);
+            const float16x8_t q06 = vld1q_f16(mtx_b0 + 24);
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vgetq_lane_f16(p00, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vgetq_lane_f16(p00, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vgetq_lane_f16(p00, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vgetq_lane_f16(p00, 3)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q02, vgetq_lane_f16(p00, 4)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q02, vgetq_lane_f16(p00, 5)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q02, vgetq_lane_f16(p00, 6)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q02, vgetq_lane_f16(p00, 7)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q04, vgetq_lane_f16(p02, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q04, vgetq_lane_f16(p02, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q04, vgetq_lane_f16(p02, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q04, vgetq_lane_f16(p02, 3)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q06, vgetq_lane_f16(p02, 4)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q06, vgetq_lane_f16(p02, 5)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q06, vgetq_lane_f16(p02, 6)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q06, vgetq_lane_f16(p02, 7)));
+
+            mtx_a0 += 16;
+            mtx_b0 += 32;
+        }
+
+        for(; mtx_b0 < mtx_b0_end_addr;)
+
+        {
+            const float16x4_t p00 = vld1_f16(mtx_a0);
+            const float16x8_t q00 = vld1q_f16(mtx_b0);
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vget_lane_f16(p00, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vget_lane_f16(p00, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vget_lane_f16(p00, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vget_lane_f16(p00, 3)));
+
+            mtx_a0 += 4;
+            mtx_b0 += 8;
+        }
+
+        if(multiply_alpha)
+        {
+            c.val[0] = vmulq_f16(c.val[0], alpha_f16);
+            c.val[1] = vmulq_f16(c.val[1], alpha_f16);
+            c.val[2] = vmulq_f16(c.val[2], alpha_f16);
+            c.val[3] = vmulq_f16(c.val[3], alpha_f16);
+        }
+
+        if(id.x() < (out_width - 8))
+        {
+            vst1q_f16(mtx_out, c.val[0]);
+            if(id.y() + 1 < out_height)
+            {
+                vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
+                if(id.y() + 2 < out_height)
+                {
+                    vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
+                    if(id.y() + 3 < out_height)
+                    {
+                        vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
+                    }
+                }
+            }
+        }
+        else
+        {
+            // Left-over columns
+            const int columns_left = out_width - id.x();
+            for(int x = 0; x < columns_left; ++x)
+            {
+                *(mtx_out + x) = c.val[0][x];
+                if(id.y() + 1 < out_height)
+                {
+                    *(mtx_out + x + 1 * out_stride) = c.val[1][x];
+                    if(id.y() + 2 < out_height)
+                    {
+                        *(mtx_out + x + 2 * out_stride) = c.val[2][x];
+                        if(id.y() + 3 < out_height)
+                        {
+                            *(mtx_out + x + 3 * out_stride) = c.val[3][x];
+                        }
+                    }
+                }
+            }
+        }
+    },
+    ina, inb, out);
+}
+
 void neon_fp16_gemm_matrix_mul(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha, const bool is_dst_vector)
 {
     return (is_dst_vector) ? vector_matrix_multiply_f16(lhs, rhs, dst, window, info, alpha) : matrix_matrix_multiply_f16(lhs, rhs, dst, window, info, alpha);
diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp
index 300dc3ffc7..0051d3d9dc 100644
--- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp
+++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2022 Arm Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -31,202 +31,6 @@ namespace arm_compute
 {
 namespace cpu
 {
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
-{
-    const auto width_matrix_b  = static_cast<int>(dst->info()->dimension(0));
-    const auto in_b_stride     = static_cast<int>(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size());
-    const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
-
-    // The implementation computes 32 elements per iteration
-    const int window_start_x = 32 * info.thread_id;
-    const int window_step_x  = 32 * info.num_threads;
-    const int window_end_x   = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
-    ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x");
-
-    Window win_out(window);
-    win_out.set(Window::DimX, Window::Dimension(0, 1, 1));
-    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
-
-    Window win_a(window);
-    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
-    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
-
-    Window win_b;
-    // 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(rhs->info()->num_dimensions() >= 3)
-    {
-        win_b = window;
-    }
-    win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
-    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
-
-    Iterator ina(lhs, win_a);
-    Iterator inb(rhs, win_b);
-    Iterator out(dst, win_out);
-
-    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
-
-    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
-
-    execute_window_loop(win_out, [&](const Coordinates &)
-    {
-        int x = window_start_x;
-        // Here we don't check for x lower equal than (window_end_x - window_step_x) because of
-        // window_end_x is computed above which may cause out-of-bound writes to the dst.
-        for(; x < (window_end_x - window_step_x); x += window_step_x)
-        {
-            if(x > width_matrix_b)
-            {
-                return;
-            }
-
-            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
-
-            float16x8_t acc0 = vdupq_n_f16(0.f);
-            float16x8_t acc1 = vdupq_n_f16(0.f);
-            float16x8_t acc2 = vdupq_n_f16(0.f);
-            float16x8_t acc3 = vdupq_n_f16(0.f);
-
-            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
-            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
-            for(; vec_a <= (vec_a_end_addr - 4);)
-            {
-                const float16x4_t a0l = vld1_f16(vec_a);
-
-                float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
-                float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
-                float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
-                float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
-                float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
-                float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
-                float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
-                float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
-
-                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0));
-                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0));
-                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0));
-                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0));
-                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1));
-                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1));
-                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1));
-                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1));
-
-                matrix_b += 2 * in_b_stride;
-
-                b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
-                b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
-                b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
-                b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
-                b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
-                b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
-                b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
-                b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
-
-                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2));
-                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2));
-                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2));
-                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2));
-                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3));
-                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3));
-                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3));
-                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3));
-
-                vec_a += 4;
-                matrix_b += 2 * in_b_stride;
-            }
-
-            for(; vec_a < vec_a_end_addr; ++vec_a)
-            {
-                const float16_t   a0  = *vec_a;
-                const float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
-                const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
-                const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
-                const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
-
-                acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0));
-                acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0));
-                acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0));
-                acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0));
-
-                matrix_b += in_b_stride;
-            }
-
-            // Multiply by the weight of matrix product (alpha)
-            if(multiply_alpha)
-            {
-                acc0 = vmulq_f16(acc0, alpha_f16);
-                acc1 = vmulq_f16(acc1, alpha_f16);
-                acc2 = vmulq_f16(acc2, alpha_f16);
-                acc3 = vmulq_f16(acc3, alpha_f16);
-            }
-
-            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
-
-            vst1q_f16(vec_out + 0, acc0);
-            vst1q_f16(vec_out + 8, acc1);
-            vst1q_f16(vec_out + 16, acc2);
-            vst1q_f16(vec_out + 24, acc3);
-        }
-
-        for(; x < window_end_x; ++x)
-        {
-            if(x > width_matrix_b)
-            {
-                return;
-            }
-
-            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
-
-            float16x4_t vacc = vdup_n_f16(0.f);
-
-            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
-            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
-            for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
-            {
-                const float16x4_t a0l = vld1_f16(vec_a);
-
-                const float16x4_t b_col =
-                {
-                    *(matrix_b + 0 * in_b_stride),
-                    *(matrix_b + 1 * in_b_stride),
-                    *(matrix_b + 2 * in_b_stride),
-                    *(matrix_b + 3 * in_b_stride),
-                };
-
-                vacc = vadd_f16(vacc, vmul_f16(a0l, b_col));
-
-                matrix_b += 4 * in_b_stride;
-            }
-
-            float16_t acc = vget_lane_f16(vacc, 0) + vget_lane_f16(vacc, 1) + vget_lane_f16(vacc, 2) + vget_lane_f16(vacc, 3);
-
-            for(; vec_a < vec_a_end_addr; ++vec_a)
-            {
-                const float16_t a0  = *vec_a;
-                const float16_t b00 = *matrix_b;
-
-                acc += b00 * a0;
-
-                matrix_b += in_b_stride;
-            }
-
-            // Multiply by the weight of matrix product (alpha)
-            if(multiply_alpha)
-            {
-                acc *= static_cast<float16_t>(alpha);
-            }
-
-            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
-
-            *(vec_out) = acc;
-        }
-    },
-    ina, inb, out);
-}
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
 void vector_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
 {
     const auto width_matrix_b  = static_cast<int>(dst->info()->dimension(0));
@@ -831,186 +635,6 @@ void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor
     },
     ina, inb, out);
 }
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
-{
-    ARM_COMPUTE_UNUSED(info);
-    const int    out_width            = static_cast<int>(dst->info()->dimension(0));
-    const int    out_height           = static_cast<int>(dst->info()->dimension(1));
-    const size_t in_b_stride          = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
-    const size_t out_stride           = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->info()->data_type());
-    const int    num_elems_matrix_b_x = rhs->info()->dimension(0);
-
-    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the dst matrix
-    Window win_a(window);
-    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
-    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
-
-    Window win_b;
-    // 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(rhs->info()->num_dimensions() >= 3)
-    {
-        win_b = window;
-    }
-    // Set step_x and step_y for matrix B. Scale by a factor of 8 the X range as the input transposed matrix A has 8 times less the cols of the dst matrix
-    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
-    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
-
-    Iterator ina(lhs, win_a);
-    Iterator inb(rhs, win_b);
-    Iterator out(dst, window);
-
-    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
-
-    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
-
-    execute_window_loop(window, [&](const Coordinates & id)
-    {
-        const auto   *mtx_a0  = reinterpret_cast<const float16_t *>(ina.ptr());
-        const auto   *mtx_b0  = reinterpret_cast<const float16_t *>(inb.ptr());
-        auto         *mtx_out = reinterpret_cast<float16_t *>(out.ptr());
-        float16x8x4_t c =
-        {
-            {
-                vdupq_n_f16(0.f),
-                vdupq_n_f16(0.f),
-                vdupq_n_f16(0.f),
-                vdupq_n_f16(0.f)
-            }
-        };
-
-        /*
-        This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
-             |a00 a01 a02 a03 | a04 a05 a06 a07|
-             |a10 a11 a12 a13 | a14 a15 a16 a17|
-             |a20 a21 a22 a23 | a24 a25 a26 a27| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | a40 a50 a60 a70 | ...
-             |a30 a31 a32 a33 | a34 a35 a36 a37|   | a04 a14 a24 a34 || a05 a15 a25 a35 || a06 a15 a26 a36 || a07 a17 a27 a37 | a44 a54 a64 a74 | ...
-             |a40 a41 a42 a43 | a44 a45 a46 a47|
-             |a50 a51 a52 a53 | a54 a55 a56 a57|
-             |a60 a61 a62 a63 | a64 a65 a66 a67|
-             |a70 a71 a72 a73 | a74 a75 a76 a77|
-
-             After this operation, the dst matrix will have the following shape: [ height * 4, width / 4 ]
-
-        B Matrix has been transposed as shown below
-
-           |b00 b01 b02 b03 b04 b05 b06 b07|
-           |b10 b11 b12 b13 b14 b15 b16 b17|
-           |b20 b21 b22 b23 b24 b25 b26 b27|
-           |b30 b31 b32 b33 b34 b35 b36 b37|
-          ------------------->
-
-           |b00 b01 b02 b03 b04 b05 b06 b07||b10 b11 b12 b13 b14 b15 b16 b17||b20 b21 b22 b23 b24 b25 b26 b27||b30 b31 b32 b33 b34 b35 b36 b37|
-
-            c.val[0][0] = a00*b00 + a01*b10 + a02*b20 + a03*b30
-            c.val[0][1] = a00*b01 + a01*b11 + a02*b21 + a03*b31
-
-        The size of the dst tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
-        */
-        const float16_t *mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
-
-        for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
-
-        {
-            const float16x8_t p00 = vld1q_f16(mtx_a0);
-            const float16x8_t p02 = vld1q_f16(mtx_a0 + 8);
-
-            const float16x8_t q00 = vld1q_f16(mtx_b0);
-            const float16x8_t q02 = vld1q_f16(mtx_b0 + 8);
-            const float16x8_t q04 = vld1q_f16(mtx_b0 + 16);
-            const float16x8_t q06 = vld1q_f16(mtx_b0 + 24);
-
-            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vgetq_lane_f16(p00, 0)));
-            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vgetq_lane_f16(p00, 1)));
-            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vgetq_lane_f16(p00, 2)));
-            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vgetq_lane_f16(p00, 3)));
-
-            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q02, vgetq_lane_f16(p00, 4)));
-            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q02, vgetq_lane_f16(p00, 5)));
-            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q02, vgetq_lane_f16(p00, 6)));
-            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q02, vgetq_lane_f16(p00, 7)));
-
-            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q04, vgetq_lane_f16(p02, 0)));
-            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q04, vgetq_lane_f16(p02, 1)));
-            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q04, vgetq_lane_f16(p02, 2)));
-            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q04, vgetq_lane_f16(p02, 3)));
-
-            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q06, vgetq_lane_f16(p02, 4)));
-            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q06, vgetq_lane_f16(p02, 5)));
-            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q06, vgetq_lane_f16(p02, 6)));
-            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q06, vgetq_lane_f16(p02, 7)));
-
-            mtx_a0 += 16;
-            mtx_b0 += 32;
-        }
-
-        for(; mtx_b0 < mtx_b0_end_addr;)
-
-        {
-            const float16x4_t p00 = vld1_f16(mtx_a0);
-            const float16x8_t q00 = vld1q_f16(mtx_b0);
-
-            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vget_lane_f16(p00, 0)));
-            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vget_lane_f16(p00, 1)));
-            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vget_lane_f16(p00, 2)));
-            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vget_lane_f16(p00, 3)));
-
-            mtx_a0 += 4;
-            mtx_b0 += 8;
-        }
-
-        if(multiply_alpha)
-        {
-            c.val[0] = vmulq_f16(c.val[0], alpha_f16);
-            c.val[1] = vmulq_f16(c.val[1], alpha_f16);
-            c.val[2] = vmulq_f16(c.val[2], alpha_f16);
-            c.val[3] = vmulq_f16(c.val[3], alpha_f16);
-        }
-
-        if(id.x() < (out_width - 8))
-        {
-            vst1q_f16(mtx_out, c.val[0]);
-            if(id.y() + 1 < out_height)
-            {
-                vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
-                if(id.y() + 2 < out_height)
-                {
-                    vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
-                    if(id.y() + 3 < out_height)
-                    {
-                        vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
-                    }
-                }
-            }
-        }
-        else
-        {
-            // Left-over columns
-            const int columns_left = out_width - id.x();
-            for(int x = 0; x < columns_left; ++x)
-            {
-                *(mtx_out + x) = c.val[0][x];
-                if(id.y() + 1 < out_height)
-                {
-                    *(mtx_out + x + 1 * out_stride) = c.val[1][x];
-                    if(id.y() + 2 < out_height)
-                    {
-                        *(mtx_out + x + 2 * out_stride) = c.val[2][x];
-                        if(id.y() + 3 < out_height)
-                        {
-                            *(mtx_out + x + 3 * out_stride) = c.val[3][x];
-                        }
-                    }
-                }
-            }
-        }
-    },
-    ina, inb, out);
-}
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
 } // namespace cpu
 
 } // namespace arm_compute
diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h
index 6bf865a624..f9f1f247ac 100644
--- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h
+++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -30,13 +30,6 @@ namespace arm_compute
 {
 namespace cpu
 {
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha);
-
-void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha);
-
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
 void vector_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha);
 
 void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha);