diff --git a/tensorflow/lite/kernels/cpu_backend_gemm.h b/tensorflow/lite/kernels/cpu_backend_gemm.h
index eccf69f19d3..6ede8d2fc49 100644
--- a/tensorflow/lite/kernels/cpu_backend_gemm.h
+++ b/tensorflow/lite/kernels/cpu_backend_gemm.h
@@ -92,6 +92,7 @@ void Gemm(const MatrixParams<LhsScalar>& lhs_params, const LhsScalar* lhs_data,
           const MatrixParams<DstScalar>& dst_params, DstScalar* dst_data,
           const GemmParams<AccumScalar, DstScalar, quantization_flavor>& params,
           CpuBackendContext* context) {
+  gemmlowp::ScopedProfilingLabel label("cpu_backend_gemm::Gemm");
   ValidateParams(lhs_params, rhs_params, dst_params, params);
   if (dst_params.cols == 1) {
     // GEMV case: try a custom fast GEMV path.
@@ -100,6 +101,7 @@ void Gemm(const MatrixParams<LhsScalar>& lhs_params, const LhsScalar* lhs_data,
       return;
     }
   }
+  gemmlowp::ScopedProfilingLabel label2("cpu_backend_gemm::Gemm: general GEMM");
   GemmImpl<LhsScalar, RhsScalar, AccumScalar, DstScalar,
            quantization_flavor>::Run(lhs_params, lhs_data, rhs_params, rhs_data,
                                      dst_params, dst_data, params, context);
diff --git a/tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h b/tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h
index 3b686e5a1f7..017f1660e8c 100644
--- a/tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h
+++ b/tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h
@@ -144,6 +144,7 @@ bool CustomGemv(
     const MatrixParams<DstScalar>& dst_params, DstScalar* dst_data,
     const GemmParams<AccumScalar, DstScalar, quantization_flavor>& params,
     CpuBackendContext* context) {
+  gemmlowp::ScopedProfilingLabel label("cpu_backend_gemm::Gemm: CustomGemv");
   using Impl = CustomGemvImpl<LhsScalar, RhsScalar, AccumScalar, DstScalar,
                               quantization_flavor>;
   if (lhs_params.rows < Impl::kKernelRows) {
@@ -186,8 +187,8 @@ bool CustomGemv(
 // Some NEON helper functions used by CustomGemvImpl specializations below,
 // allowing for some type genericity in them.
 
-inline int16x8x2_t LoadAndSubtractZeroPoint(const std::uint8_t* src,
-                                            std::uint8_t zero_point) {
+inline int16x8x2_t Load16AndSubtractZeroPoint(const std::uint8_t* src,
+                                              std::uint8_t zero_point) {
   uint8x16_t src_u8 = vld1q_u8(src);
   int16x8_t src_s16_0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(src_u8)));
   int16x8_t src_s16_1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(src_u8)));
@@ -198,8 +199,8 @@ inline int16x8x2_t LoadAndSubtractZeroPoint(const std::uint8_t* src,
   return result;
 }
 
-inline int16x8x2_t LoadAndSubtractZeroPoint(const std::int8_t* src,
-                                            std::int8_t zero_point) {
+inline int16x8x2_t Load16AndSubtractZeroPoint(const std::int8_t* src,
+                                              std::int8_t zero_point) {
   int8x16_t src_s8 = vld1q_s8(src);
   int16x8_t src_s16_0 = vmovl_s8(vget_low_s8(src_s8));
   int16x8_t src_s16_1 = vmovl_s8(vget_high_s8(src_s8));
@@ -210,6 +211,22 @@ inline int16x8x2_t LoadAndSubtractZeroPoint(const std::int8_t* src,
   return result;
 }
 
+inline int16x8_t Load8AndSubtractZeroPoint(const std::uint8_t* src,
+                                           std::uint8_t zero_point) {
+  uint8x8_t src_u8 = vld1_u8(src);
+  int16x8_t src_s16 = vreinterpretq_s16_u16(vmovl_u8(src_u8));
+  int16x8_t zero_point_vec = vdupq_n_s16(zero_point);
+  return vsubq_s16(src_s16, zero_point_vec);
+}
+
+inline int16x8_t Load8AndSubtractZeroPoint(const std::int8_t* src,
+                                           std::int8_t zero_point) {
+  int8x8_t src_s8 = vld1_s8(src);
+  int16x8_t src_s16 = vmovl_s8(src_s8);
+  int16x8_t zero_point_vec = vdupq_n_s16(zero_point);
+  return vsubq_s16(src_s16, zero_point_vec);
+}
+
 inline void ClampAndStore(int32x4_t src, std::uint8_t clamp_min,
                           std::uint8_t clamp_max, std::uint8_t* dst) {
   // Narrow values down to 16 bit signed.
@@ -288,11 +305,12 @@ struct CustomGemvImpl<LhsScalar, RhsScalar, std::int32_t, DstScalar,
       const MatrixParams<RhsScalar>& rhs_params,
       const MatrixParams<DstScalar>& dst_params,
       const GemmParams<std::int32_t, DstScalar, quantization_flavor>& params) {
-    // There are no further requirements on the applicability of this kernel,
-    // beyond the left-hand-side matrix having at least kKernelRows rows,
-    // and the type requirements implied in this template partial
-    // specialization.
-    return true;
+    // The kernel processes at least 8 LHS columns at once to fill NEON
+    // registers. The leftovers-handling code at the end works by loading a
+    // partially overlapping final register by walking back by a few (<8) values
+    // to avoid running past the row's end. This relies on there being
+    // at least 8 LHS columns.
+    return lhs_params.cols >= 8;
   }
 
   static void Run(
@@ -311,6 +329,27 @@ struct CustomGemvImpl<LhsScalar, RhsScalar, std::int32_t, DstScalar,
       // `row`.
       row = std::min(row, row_end - kKernelRows);
       const LhsScalar* filter_ptr = lhs_data + row * lhs_params.cols;
+
+      static constexpr int kCacheLineSize = 64;
+      for (int k = 0; k < rhs_params.rows;
+           k += kCacheLineSize / sizeof(RhsScalar)) {
+        optimized_ops_preload_l1_keep(rhs_data + k);
+      }
+
+      // kPreloadAhead is empirically determined.
+      // End-to-end latency (ms) on mobilenet_v2_0.35_96_8bit, 1 thread,
+      // on Qualcomm S855:
+      //
+      // kPreloadAhead | big core | little core
+      // --------------+----------+------------
+      // 64            | 1.26     | 5.45
+      // 128           | 1.23     | 5.01
+      // 256           | 1.18     | 4.9
+      // 512           | 1.18     | 5.45
+      // 1024          | 1.18     | 6.5
+      // no prefetch   | 1.25     | 8.1
+      static constexpr int kPreloadAhead = 256;
+
       // 4 accumulator registers, one for each row being processed.
       // Each has 4 int32 lanes that corresponds to columns modulo 4, and
       // will need to be horizontally reduced at the end.
@@ -322,16 +361,28 @@ struct CustomGemvImpl<LhsScalar, RhsScalar, std::int32_t, DstScalar,
       // As much as possible, handle 16 columns of the left-hand side matrix
       // at a time. This allows for decent NEON implementation.
       for (; in <= lhs_params.cols - 16; in += 16) {
+        const LhsScalar* local_filter_ptr = filter_ptr;
         int16x8x2_t input_val =
-            LoadAndSubtractZeroPoint(rhs_data + in, rhs_params.zero_point);
-        int16x8x2_t filter_val_0 = LoadAndSubtractZeroPoint(
-            filter_ptr + 0 * lhs_params.cols, lhs_params.zero_point);
-        int16x8x2_t filter_val_1 = LoadAndSubtractZeroPoint(
-            filter_ptr + 1 * lhs_params.cols, lhs_params.zero_point);
-        int16x8x2_t filter_val_2 = LoadAndSubtractZeroPoint(
-            filter_ptr + 2 * lhs_params.cols, lhs_params.zero_point);
-        int16x8x2_t filter_val_3 = LoadAndSubtractZeroPoint(
-            filter_ptr + 3 * lhs_params.cols, lhs_params.zero_point);
+            Load16AndSubtractZeroPoint(rhs_data + in, rhs_params.zero_point);
+        int16x8x2_t filter_val_0 =
+            Load16AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(LhsScalar));
+        local_filter_ptr += lhs_params.cols;
+        int16x8x2_t filter_val_1 =
+            Load16AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(LhsScalar));
+        local_filter_ptr += lhs_params.cols;
+        int16x8x2_t filter_val_2 =
+            Load16AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(LhsScalar));
+        local_filter_ptr += lhs_params.cols;
+        int16x8x2_t filter_val_3 =
+            Load16AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(LhsScalar));
         filter_ptr += 16;
         acc0 = vmlal_s16(acc0, vget_low_s16(filter_val_0.val[0]),
                          vget_low_s16(input_val.val[0]));
@@ -366,27 +417,109 @@ struct CustomGemvImpl<LhsScalar, RhsScalar, std::int32_t, DstScalar,
         acc3 = vmlal_s16(acc3, vget_high_s16(filter_val_3.val[1]),
                          vget_high_s16(input_val.val[1]));
       }
-      // Leftovers: fewer than 16 columns remain. Very slow code, could be
-      // improved upon if critical in some application.
+      // Less that 16 values remain. Try to handle 8 more.
+      if (in <= lhs_params.cols - 8) {
+        int16x8_t input_val =
+            Load8AndSubtractZeroPoint(rhs_data + in, rhs_params.zero_point);
+        int16x8_t filter_val_0 = Load8AndSubtractZeroPoint(
+            filter_ptr + 0 * lhs_params.cols, lhs_params.zero_point);
+        int16x8_t filter_val_1 = Load8AndSubtractZeroPoint(
+            filter_ptr + 1 * lhs_params.cols, lhs_params.zero_point);
+        int16x8_t filter_val_2 = Load8AndSubtractZeroPoint(
+            filter_ptr + 2 * lhs_params.cols, lhs_params.zero_point);
+        int16x8_t filter_val_3 = Load8AndSubtractZeroPoint(
+            filter_ptr + 3 * lhs_params.cols, lhs_params.zero_point);
+        filter_ptr += 8;
+        acc0 = vmlal_s16(acc0, vget_low_s16(filter_val_0),
+                         vget_low_s16(input_val));
+        acc1 = vmlal_s16(acc1, vget_low_s16(filter_val_1),
+                         vget_low_s16(input_val));
+        acc2 = vmlal_s16(acc2, vget_low_s16(filter_val_2),
+                         vget_low_s16(input_val));
+        acc3 = vmlal_s16(acc3, vget_low_s16(filter_val_3),
+                         vget_low_s16(input_val));
+        acc0 = vmlal_s16(acc0, vget_high_s16(filter_val_0),
+                         vget_high_s16(input_val));
+        acc1 = vmlal_s16(acc1, vget_high_s16(filter_val_1),
+                         vget_high_s16(input_val));
+        acc2 = vmlal_s16(acc2, vget_high_s16(filter_val_2),
+                         vget_high_s16(input_val));
+        acc3 = vmlal_s16(acc3, vget_high_s16(filter_val_3),
+                         vget_high_s16(input_val));
+        in += 8;
+      }
+      // Less than 8 values remain. Handle the remaining values
+      // in one more copy of the above code handling 8, where we
+      // walk back a few values to be able to load 8 values without
+      // overrunning the buffer. This is where we make use of the requirement
+      // (see IsSupportedGivenSufficientlyManyRows) that there at least
+      // 8 LHS columns.
       if (in < lhs_params.cols) {
-        int32 buf[16];
-        vst1q_s32(buf + 0, acc0);
-        vst1q_s32(buf + 4, acc1);
-        vst1q_s32(buf + 8, acc2);
-        vst1q_s32(buf + 12, acc3);
-        for (; in < lhs_params.cols; in++) {
-          int lane = (in + 16 - lhs_params.cols) % 4;
-          const int32 input_val = rhs_data[in] - rhs_params.zero_point;
-          for (int k = 0; k < 4; k++) {
-            int32 filter_val = lhs_data[in + (row + k) * lhs_params.cols] -
-                               lhs_params.zero_point;
-            buf[lane + 4 * k] += filter_val * input_val;
-          }
+        // `back` is how many entries to walk back by.
+        // Its value is necessarily between 1 and 7.
+        const int back = in + 8 - lhs_params.cols;
+        TFLITE_DCHECK_GE(back, 1);
+        TFLITE_DCHECK_LE(back, 7);
+        // Load 8 values as usual.
+        int16x8_t input_val = Load8AndSubtractZeroPoint(
+            rhs_data + lhs_params.cols - 8, rhs_params.zero_point);
+        const LhsScalar* local_filter_ptr = filter_ptr - back;
+        filter_ptr += lhs_params.cols - in;
+        int16x8_t filter_val_0 =
+            Load8AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        local_filter_ptr += lhs_params.cols;
+        int16x8_t filter_val_1 =
+            Load8AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        local_filter_ptr += lhs_params.cols;
+        int16x8_t filter_val_2 =
+            Load8AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        local_filter_ptr += lhs_params.cols;
+        int16x8_t filter_val_3 =
+            Load8AndSubtractZeroPoint(local_filter_ptr, lhs_params.zero_point);
+        // Now zero out the `back` first entries of input_val.
+        // vsetq_lane_s16 takes a literal index, so we need unrolled code.
+        switch (back) {
+          case 7:
+            input_val = vsetq_lane_s16(0, input_val, 6);
+            [[clang::fallthrough]];
+          case 6:
+            input_val = vsetq_lane_s16(0, input_val, 5);
+            [[clang::fallthrough]];
+          case 5:
+            input_val = vsetq_lane_s16(0, input_val, 4);
+            [[clang::fallthrough]];
+          case 4:
+            input_val = vsetq_lane_s16(0, input_val, 3);
+            [[clang::fallthrough]];
+          case 3:
+            input_val = vsetq_lane_s16(0, input_val, 2);
+            [[clang::fallthrough]];
+          case 2:
+            input_val = vsetq_lane_s16(0, input_val, 1);
+            [[clang::fallthrough]];
+          default:
+            input_val = vsetq_lane_s16(0, input_val, 0);
         }
-        acc0 = vld1q_s32(buf + 0);
-        acc1 = vld1q_s32(buf + 4);
-        acc2 = vld1q_s32(buf + 8);
-        acc3 = vld1q_s32(buf + 12);
+        // Multiply-accumulate 8 values as usual. The `back` first lanes
+        // of filter_val_* are junk, but it doesn't matter since they get
+        // multiplied by the zeros that we just wrote in the corresponding
+        // lanes of input_val.
+        acc0 = vmlal_s16(acc0, vget_low_s16(filter_val_0),
+                         vget_low_s16(input_val));
+        acc1 = vmlal_s16(acc1, vget_low_s16(filter_val_1),
+                         vget_low_s16(input_val));
+        acc2 = vmlal_s16(acc2, vget_low_s16(filter_val_2),
+                         vget_low_s16(input_val));
+        acc3 = vmlal_s16(acc3, vget_low_s16(filter_val_3),
+                         vget_low_s16(input_val));
+        acc0 = vmlal_s16(acc0, vget_high_s16(filter_val_0),
+                         vget_high_s16(input_val));
+        acc1 = vmlal_s16(acc1, vget_high_s16(filter_val_1),
+                         vget_high_s16(input_val));
+        acc2 = vmlal_s16(acc2, vget_high_s16(filter_val_2),
+                         vget_high_s16(input_val));
+        acc3 = vmlal_s16(acc3, vget_high_s16(filter_val_3),
+                         vget_high_s16(input_val));
       }
 
       // Horizontally reduce accumulators
@@ -484,11 +617,12 @@ struct CustomGemvImpl<float, float, float, float,
       const MatrixParams<float>& rhs_params,
       const MatrixParams<float>& dst_params,
       const GemmParams<float, float>& params) {
-    // There are no further requirements on the applicability of this kernel,
-    // beyond the left-hand-side matrix having at least kKernelRows rows,
-    // and the type requirements implied in this template partial
-    // specialization.
-    return true;
+    // The kernel processes 4 LHS columns at once to fill float32x4 registers.
+    // The leftovers-handling code at the end works by loading a partially
+    // overlapping final register by walking back by a few (<4) floats
+    // to avoid running past the row's end. This relies on there being
+    // at least 4 LHS columns.
+    return lhs_params.cols >= 4;
   }
   static void Run(const MatrixParams<float>& lhs_params, const float* lhs_data,
                   const MatrixParams<float>& rhs_params, const float* rhs_data,
@@ -505,6 +639,27 @@ struct CustomGemvImpl<float, float, float, float,
       // `row`.
       row = std::min(row, row_end - kKernelRows);
       const float* filter_ptr = lhs_data + row * lhs_params.cols;
+
+      static constexpr int kCacheLineSize = 64;
+      for (int k = 0; k < rhs_params.rows;
+           k += kCacheLineSize / sizeof(float)) {
+        optimized_ops_preload_l1_keep(rhs_data + k);
+      }
+
+      // kPreloadAhead is empirically determined.
+      // End-to-end latency (ms) on mobilenet_v2_0.35_96_float, 1 thread,
+      // on Qualcomm S855:
+      //
+      // kPreloadAhead | big core | little core
+      // --------------+----------+------------
+      // 64            | 2.4      | 15.2
+      // 128           | 2.15     | 12.9
+      // 256           | 2        | 12.9
+      // 512           | 2.08     | 13.3
+      // 1024          | 2.05     | 14.7
+      // no prefetch   | 2.1      | 28
+      static constexpr int kPreloadAhead = 256;
+
       // 4 accumulator registers, one for each row being processed.
       // Each has 4 float32 lanes that corresponds to columns modulo 4, and
       // will need to be horizontally reduced at the end.
@@ -517,36 +672,71 @@ struct CustomGemvImpl<float, float, float, float,
       // at a time. This allows for decent NEON implementation.
       for (; in <= lhs_params.cols - 4; in += 4) {
         float32x4_t input_val = vld1q_f32(rhs_data + in);
-        float32x4_t filter_val_0 = vld1q_f32(filter_ptr + 0 * lhs_params.cols);
-        float32x4_t filter_val_1 = vld1q_f32(filter_ptr + 1 * lhs_params.cols);
-        float32x4_t filter_val_2 = vld1q_f32(filter_ptr + 2 * lhs_params.cols);
-        float32x4_t filter_val_3 = vld1q_f32(filter_ptr + 3 * lhs_params.cols);
+        const float* local_filter_ptr = filter_ptr;
+        float32x4_t filter_val_0 = vld1q_f32(local_filter_ptr);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(float));
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_1 = vld1q_f32(local_filter_ptr);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(float));
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_2 = vld1q_f32(local_filter_ptr);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(float));
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_3 = vld1q_f32(local_filter_ptr);
+        optimized_ops_preload_l1_stream(local_filter_ptr +
+                                        kPreloadAhead / sizeof(float));
         filter_ptr += 4;
         acc0 = mul_add(acc0, filter_val_0, input_val);
         acc1 = mul_add(acc1, filter_val_1, input_val);
         acc2 = mul_add(acc2, filter_val_2, input_val);
         acc3 = mul_add(acc3, filter_val_3, input_val);
       }
-      // Leftovers: fewer than 4 columns remain. Very slow code, could be
-      // improved upon if critical in some application.
+      // Less than 4 values remain. Handle the remaining values
+      // in one more copy of the above code handling 4, where we
+      // walk back a few values to be able to load 4 values without
+      // overrunning the buffer. This is where we make use of the requirement
+      // (see IsSupportedGivenSufficientlyManyRows) that there at least
+      // 4 LHS columns.
       if (in < lhs_params.cols) {
-        float buf[16];
-        vst1q_f32(buf + 0, acc0);
-        vst1q_f32(buf + 4, acc1);
-        vst1q_f32(buf + 8, acc2);
-        vst1q_f32(buf + 12, acc3);
-        for (; in < lhs_params.cols; in++) {
-          int lane = (in + 4 - lhs_params.cols) % 4;
-          const float input_val = rhs_data[in];
-          for (int k = 0; k < 4; k++) {
-            float filter_val = lhs_data[in + (row + k) * lhs_params.cols];
-            buf[lane + 4 * k] += filter_val * input_val;
-          }
+        // `back` is how many entries to walk back by.
+        // Its value is necessarily between 1 and 3.
+        const int back = in + 4 - lhs_params.cols;
+        TFLITE_DCHECK_GE(back, 1);
+        TFLITE_DCHECK_LE(back, 3);
+        // Load 4 values as usual.
+        float32x4_t input_val = vld1q_f32(rhs_data + lhs_params.cols - 4);
+        const float* local_filter_ptr = filter_ptr - back;
+        filter_ptr += lhs_params.cols - in;
+        float32x4_t filter_val_0 = vld1q_f32(local_filter_ptr);
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_1 = vld1q_f32(local_filter_ptr);
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_2 = vld1q_f32(local_filter_ptr);
+        local_filter_ptr += lhs_params.cols;
+        float32x4_t filter_val_3 = vld1q_f32(local_filter_ptr);
+        // Now zero out the `back` first entries of input_val.
+        // vsetq_lane_f32 takes a literal index, so we need unrolled code.
+        switch (back) {
+          case 3:
+            input_val = vsetq_lane_f32(0, input_val, 2);
+            [[clang::fallthrough]];
+          case 2:
+            input_val = vsetq_lane_f32(0, input_val, 1);
+            [[clang::fallthrough]];
+          default:
+            input_val = vsetq_lane_f32(0, input_val, 0);
         }
-        acc0 = vld1q_f32(buf + 0);
-        acc1 = vld1q_f32(buf + 4);
-        acc2 = vld1q_f32(buf + 8);
-        acc3 = vld1q_f32(buf + 12);
+        // Multiply-accumulate 4 values as usual. The `back` first lanes
+        // of filter_val_* are junk, but it doesn't matter since they get
+        // multiplied by the zeros that we just wrote in the corresponding
+        // lanes of input_val.
+        acc0 = mul_add(acc0, filter_val_0, input_val);
+        acc1 = mul_add(acc1, filter_val_1, input_val);
+        acc2 = mul_add(acc2, filter_val_2, input_val);
+        acc3 = mul_add(acc3, filter_val_3, input_val);
       }
 
       // Horizontally reduce accumulators
diff --git a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/lite/kernels/internal/optimized/optimized_ops.h
index b4171de7395..ef077a789df 100644
--- a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/lite/kernels/internal/optimized/optimized_ops.h
@@ -1205,6 +1205,7 @@ inline void Conv(const ConvParams& params, const RuntimeShape& input_shape,
                  uint8* output_data, const RuntimeShape& im2col_shape,
                  uint8* im2col_data, CpuBackendContext* cpu_backend_context) {
   gemmlowp::ScopedProfilingLabel label("Conv/8bit");
+
   const int stride_width = params.stride_width;
   const int stride_height = params.stride_height;
   const int dilation_width_factor = params.dilation_width_factor;