diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD
index 6481a8a..e4d533b 100644
--- a/tflm/opt/BUILD
+++ b/tflm/opt/BUILD
@@ -19,6 +19,7 @@
     srcs = [
         "conv.cc",
         "depthwise_conv_s16.cc",
+        "depthwise_conv_s8.cc",
         "elementwise_add_s16.cc",
         "elementwise_add_s32.cc",
         "elementwise_add_s8.cc",
@@ -35,6 +36,7 @@
     deps = [
         "//crt",
         "@tflite-micro//tensorflow/lite/kernels/internal:common",
+        "@tflite-micro//tensorflow/lite/kernels/internal:reference_base",
     ],
     alwayslink = True,
 )
diff --git a/tflm/opt/depthwise_conv_s8.cc b/tflm/opt/depthwise_conv_s8.cc
new file mode 100644
index 0000000..c4ee35a
--- /dev/null
+++ b/tflm/opt/depthwise_conv_s8.cc
@@ -0,0 +1,194 @@
+/*
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <algorithm>
+
+#include "crt/kelvin.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h"
+#include "tensorflow/lite/kernels/internal/runtime_shape.h"
+#include "tensorflow/lite/kernels/internal/types.h"
+#include "tflm/opt/opt.h"
+
+namespace kelvin::opt {
+
+void Swizzle(const int32_t* input, int32_t* output, int N) {
+  const int32_t(&in)[N] = *(int32_t(*)[N])input;
+  int32_t(&out)[N * 4] = *(int32_t(*)[N * 4]) output;
+  // Convert to accumulator swizzle pattern.
+  for (int i = 0; i < N / 8; ++i) {
+    int32_t* out0 = out + i * 32 + 0;
+    int32_t* out1 = out + i * 32 + 16;
+    int32_t* out2 = out + i * 32 + 8;
+    int32_t* out3 = out + i * 32 + 24;
+    for (int j = 0; j < 4; ++j) {
+      const int32_t* p_in = in + i * 8;
+      for (int k = 0; k < 2; ++k) {
+        *out0++ = *p_in++;
+        *out1++ = *p_in++;
+        *out2++ = *p_in++;
+        *out3++ = *p_in++;
+      }
+    }
+  }
+}
+
+void DWConv2DKelvin_d32(
+    const tflite::DepthwiseParams& params, const int32_t* output_multiplier,
+    const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
+    const int8_t* input_data, const tflite::RuntimeShape& filter_shape,
+    const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
+    const int32_t* bias_data, const tflite::RuntimeShape& output_shape,
+    int8_t* output_data
+
+) {
+  const int stride_width = params.stride_width;
+  const int stride_height = params.stride_height;
+  const int dilation_width_factor = params.dilation_width_factor;
+  const int dilation_height_factor = params.dilation_height_factor;
+  const int pad_width = params.padding_values.width;
+  const int pad_height = params.padding_values.height;
+  const int32_t input_offset = params.input_offset;
+  const int32_t output_offset = params.output_offset;
+  const int32_t output_activation_min = params.quantized_activation_min;
+  const int32_t output_activation_max = params.quantized_activation_max;
+  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+  const int input_height = input_shape.Dims(1);
+  const int input_width = input_shape.Dims(2);
+  const int input_depth = input_shape.Dims(3);
+  const int filter_height = filter_shape.Dims(1);
+  const int filter_width = filter_shape.Dims(2);
+  const int output_height = output_shape.Dims(1);
+  const int output_width = output_shape.Dims(2);
+  int32_t swizzled_bias_data[32 * 4];
+  int32_t swizzled_shift_multi[32 * 4];
+  int32_t swizzled_output_multi[32 * 4];
+
+  for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) {
+    const int output_channel = in_channel;
+    Swizzle(bias_data + output_channel, swizzled_bias_data, 32);
+    Swizzle(output_multiplier + output_channel, swizzled_output_multi, 32);
+    Swizzle(output_shift + output_channel, swizzled_shift_multi, 32);
+
+    vld_w_x_m(v20, swizzled_bias_data);
+    vld_w_x_m(v24, swizzled_output_multi);
+    vld_w_x_m(v28, swizzled_shift_multi);
+    vrsub_w_vx_m(v28, v28, 0);
+
+    for (int batch = 0; batch < batches; ++batch) {
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          const int in_x_origin = (out_x * stride_width) - pad_width;
+          const int in_y_origin = (out_y * stride_height) - pad_height;
+
+          for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+            const int in_y = in_y_origin + filter_y;
+            if ((in_y < 0) || (in_y >= input_height)) {
+              continue;
+            }
+            for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+              const int in_x = in_x_origin + filter_x;
+              if ((in_x < 0) || (in_x >= input_width)) {
+                continue;
+              }
+
+              vld_b_x(v0, &input_data[tflite::Offset(input_shape, batch, in_y,
+                                                     in_x, in_channel)]);  // xp
+              vld_b_x(v4, &filter_data[tflite::Offset(filter_shape, 0, filter_y,
+                                                      filter_x, in_channel)]);
+
+              vaddw_h_vx(v0, v0, 0);
+              vadd_h_vx(v0, v0, static_cast<int16_t>(input_offset));
+              vadd_h_vx(v1, v1,
+                        static_cast<int16_t>(input_offset));  // v0 v1 input
+
+              vaddw_h_vx(v4, v4, static_cast<int16_t>(0));
+              vmulw_w_vv(v8, v0, v4);
+              vmulw_w_vv(v10, v1, v5);
+
+              vadd_w_vv_m(v48, v48, v8);
+            }
+          }
+
+          vadd_w_vv_m(v48, v48, v20);  // add bias
+          vdmulh_w_r_vv_m(v48, v48, v24);
+          vsha_w_r_vv_m(v48, v48, v28);
+          vadd_w_vx_m(v48, v48, output_offset);
+          vmax_w_vx_m(v48, v48, output_activation_min);
+          vmin_w_vx_m(v48, v48, output_activation_max);
+          vsraqs_b_vx(v48, v48, 0);
+          vst_b_x(v48, &output_data[tflite::Offset(output_shape, batch, out_y,
+                                                   out_x, output_channel)]);
+        }
+      }
+    }
+  }
+}
+
+void DepthwiseConv2DKelvin(
+    const tflite::DepthwiseParams& params, const int32_t* output_multiplier,
+    const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
+    const int8_t* input_data, const tflite::RuntimeShape& filter_shape,
+    const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
+    const int32_t* bias_data, const tflite::RuntimeShape& output_shape,
+    int8_t* output_data) {
+  // Get parameters.
+  // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro.
+  const int stride_width = params.stride_width;
+  const int stride_height = params.stride_height;
+  const int dilation_width_factor = params.dilation_width_factor;
+  const int dilation_height_factor = params.dilation_height_factor;
+  const int pad_width = params.padding_values.width;
+  const int pad_height = params.padding_values.height;
+  const int depth_multiplier = params.depth_multiplier;
+  const int32_t input_offset = params.input_offset;
+  const int32_t output_offset = params.output_offset;
+  const int32_t output_activation_min = params.quantized_activation_min;
+  const int32_t output_activation_max = params.quantized_activation_max;
+
+  // Check dimensions of the tensors.
+  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
+  TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
+  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
+
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+  const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
+  const int input_height = input_shape.Dims(1);
+  const int input_width = input_shape.Dims(2);
+  const int input_depth = input_shape.Dims(3);
+  const int filter_height = filter_shape.Dims(1);
+  const int filter_width = filter_shape.Dims(2);
+  const int output_height = output_shape.Dims(1);
+  const int output_width = output_shape.Dims(2);
+  TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
+  TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
+
+  if (depth_multiplier == 1 && pad_height < 2 && pad_width < 2 &&
+      dilation_height_factor == 1 && dilation_width_factor == 1 &&
+      stride_height == 1 && stride_width == 1 && output_depth % 32 == 0) {
+    DWConv2DKelvin_d32(params, output_multiplier, output_shift, input_shape,
+                       input_data, filter_shape, filter_data, bias_shape,
+                       bias_data, output_shape, output_data);
+    return;
+  }
+  tflite::reference_integer_ops::DepthwiseConvPerChannel(
+      params, output_multiplier, output_shift, input_shape, input_data,
+      filter_shape, filter_data, bias_shape, bias_data, output_shape,
+      output_data);
+  return;
+}
+}  // namespace kelvin::opt
\ No newline at end of file
diff --git a/tflm/opt/opt.h b/tflm/opt/opt.h
index 9f83bf6..277f338 100644
--- a/tflm/opt/opt.h
+++ b/tflm/opt/opt.h
@@ -24,7 +24,7 @@
 /* clang-format on */
 
 namespace kelvin::opt {
-void *memcpy(void *dst, const void *src, size_t n);
+void* memcpy(void* dst, const void* src, size_t n);
 void elementwise_add_s8(const int8_t* input1, const int8_t* input2,
                         const int32_t input1_offset, const int32_t input1_mult,
                         const int32_t input1_shift, const int32_t input2_offset,
@@ -115,13 +115,29 @@
     const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
     const int32_t* bias_data, const tflite::RuntimeShape& output_shape,
     int8_t* output_data);
+void DepthwiseConv2DKelvin(
+    const tflite::DepthwiseParams& params, const int32_t* output_multiplier,
+    const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
+    const int8_t* input_data, const tflite::RuntimeShape& filter_shape,
+    const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
+    const int32_t* bias_data, const tflite::RuntimeShape& output_shape,
+    int8_t* output_data);
+void DWConv2DKelvin_d32(
+    const tflite::DepthwiseParams& params, const int32_t* output_multiplier,
+    const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
+    const int8_t* input_data, const tflite::RuntimeShape& filter_shape,
+    const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
+    const int32_t* bias_data, const tflite::RuntimeShape& output_shape,
+    int8_t* output_data);
 void DepthwiseConv2DKelvinS16K3x1(
     const int16_t* activations, const int8_t* weights, const int64_t* biases,
     int channels, int frames, int dilation, const int32_t* output_mult,
     const int32_t* output_shift, int32_t output_activation_min,
     int32_t output_activation_max, int16_t* output);
-void MaxPoolGeneric(const tflite::PoolParams& params, const tflite::RuntimeShape& input_shape,
-                    const int8_t* input_data, const tflite::RuntimeShape& output_shape,
+void MaxPoolGeneric(const tflite::PoolParams& params,
+                    const tflite::RuntimeShape& input_shape,
+                    const int8_t* input_data,
+                    const tflite::RuntimeShape& output_shape,
                     int8_t* output_data);
 
 }  // namespace kelvin::opt