COMPMID-3852: Fix complex multiplication remove padding performance regression

Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I2605baba63c9cca0370328860313b8ec09e04fb6
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4355
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 30 insertions, 8 deletions

diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
index 8d17651f37..f646ea5db7 100644
--- a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
+++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
@@ -1035,6 +1035,8 @@ void c_mul_F32_F32_F32_n(const ITensor *in1, const ITensor *in2, ITensor *out, c
     const auto    window_end_x          = static_cast<int>(window.x().end());
     const bool    is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
 
+    using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type;
+
     if(is_broadcast_across_x)
     {
         const bool     is_broadcast_input_2 = input2_win.x().step() == 0;
@@ -1057,16 +1059,38 @@ void c_mul_F32_F32_F32_n(const ITensor *in1, const ITensor *in2, ITensor *out, c
 
             const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
 
+            // Compute window_step_x elements per iteration
             int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
+            {
+                const auto  a = wrapper::vloadq(non_broadcast_input_ptr + 2 * x);
+                float32x4_t b = vdupq_n_f32(broadcast_value);
+
+                const float32x4_t mask  = { -1.0f, 1.0f, -1.0f, 1.0f };
+                const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{});
+                const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{});
+                const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{});
+                const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{});
+
+                const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10);
+                const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11);
+
+                float32x4_t res = wrapper::vmul(tmp0, b);
+                b               = wrapper::vmul(b, mask);
+
+                res = wrapper::vmla(res, tmp1, b);
+                wrapper::vstore(output_ptr + 2 * x, res);
+            }
+
             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
-                const auto broadcast_value0 = *(non_broadcast_input_ptr + 2 * x);
-                const auto broadcast_value1 = *(non_broadcast_input_ptr + 2 * x + 1);
-                auto       res1             = broadcast_value * (broadcast_value0 - broadcast_value1);
-                auto       res2             = broadcast_value * (broadcast_value1 + broadcast_value0);
-                *(output_ptr + 2 * x)       = res1;
-                *(output_ptr + 2 * x + 1)   = res2;
+                const auto non_broadcast_value0 = *(non_broadcast_input_ptr + 2 * x);
+                const auto non_broadcast_value1 = *(non_broadcast_input_ptr + 2 * x + 1);
+                auto       res1                 = broadcast_value * (non_broadcast_value0 - non_broadcast_value1);
+                auto       res2                 = broadcast_value * (non_broadcast_value1 + non_broadcast_value0);
+                *(output_ptr + 2 * x)           = res1;
+                *(output_ptr + 2 * x + 1)       = res2;
             }
         },
         broadcast_input, non_broadcast_input, output);
@@ -1087,8 +1111,6 @@ void c_mul_F32_F32_F32_n(const ITensor *in1, const ITensor *in2, ITensor *out, c
             const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
             const auto output_ptr = reinterpret_cast<float *>(output.ptr());
 
-            using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type;
-
             // Compute window_step_x elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)