diff --git a/tflm/opt/depthwise_conv_s8.cc b/tflm/opt/depthwise_conv_s8.cc
index 9e15b3c..a130324 100644
--- a/tflm/opt/depthwise_conv_s8.cc
+++ b/tflm/opt/depthwise_conv_s8.cc
@@ -22,6 +22,7 @@
 
 namespace kelvin::opt {
 namespace {
+
 // Reorders a vector to match the pattern after double-widening.
 // N must be a multiple of 4.
 void VectorSwizzle(const int32_t* input, int32_t* output, int N) {
@@ -40,6 +41,171 @@
     *out3 = *p_in++;
   }
 }
+// special case of input depth = 32n, filter shape of 3x3
+void DepthwiseConvS83x3D32(
+    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 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 output_height = output_shape.Dims(1);
+  const int output_width = output_shape.Dims(2);
+  const int output_depth = output_shape.Dims(3);
+  int32_t swizzled_bias_data[32];
+  int32_t swizzled_shift_multi[32];
+  int32_t swizzled_output_multi[32];
+
+  for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) {
+    const int output_channel = in_channel;
+    VectorSwizzle(bias_data + output_channel, swizzled_bias_data, 32);
+    VectorSwizzle(output_multiplier + output_channel, swizzled_output_multi, 32);
+    VectorSwizzle(output_shift + output_channel, swizzled_shift_multi, 32);
+
+    vld_w_x_m(v52, swizzled_bias_data);
+    vld_w_x_m(v56, swizzled_output_multi);
+    vld_w_x_m(v60, swizzled_shift_multi);
+    vrsub_w_vx_m(v60, v60, 0);
+
+    union {
+      vdwconv_u8_t dwconv;
+      uint32_t raw;
+    } cmds;
+    cmds.raw = 0;
+    cmds.dwconv.sdata1 = true;
+    cmds.dwconv.sbias1 = input_offset;
+    cmds.dwconv.sdata2 = true;
+    cmds.dwconv.sbias2 = 0;
+    cmds.dwconv.mode = 0;
+    cmds.dwconv.sparsity = 0;
+    cmds.dwconv.regbase = 0;
+
+    // Don't reorder me, otherwise data will not be
+    // loaded in the correct order
+    // (we can reuse the p_flt* due to the `p` vld variant).
+    const int8_t* p_flt0 = filter_data + in_channel;
+    const int8_t* p_flt1 = p_flt0 + input_depth;
+    const int32_t stride = 2 * input_depth;
+    vld_b_sp_xx(v6, p_flt0, stride);
+    vld_b_sp_xx(v7, p_flt1, stride);
+    vld_b_sp_xx(v8, p_flt0, stride);
+    vld_b_sp_xx(v9, p_flt1, stride);
+    vld_b_sp_xx(v10, p_flt0, stride);
+    vld_b_sp_xx(v11, p_flt1, stride);
+    vld_b_sp_xx(v12, p_flt0, stride);
+    vld_b_sp_xx(v13, p_flt1, stride);
+    vld_b_sp_xx(v14, p_flt0, stride);
+
+    for (int batch = 0; batch < batches; ++batch) {
+      const int8_t* p_output = output_data + (batch * output_width * output_height * output_depth) + output_channel;
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        const int in_y_origin = (out_y * stride_height) - pad_height;
+        const int y_offset = (output_depth * output_width * out_y);
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          const int in_x_origin = (out_x * stride_width) - pad_width;
+
+          // Initialize accumulators w/ bias data.
+          vmv_v_m(v48, v52);
+
+          bool top_pad = in_y_origin < 0;
+          bool left_pad = in_x_origin < 0;
+          bool bottom_pad = (in_y_origin + 2) >= input_height;
+          bool right_pad = (in_x_origin + 2) >= input_width;
+          bool padding_required = top_pad || left_pad || bottom_pad || right_pad;
+          const int8_t* p_in_0 = input_data +
+            (batch * input_height * input_width * input_depth) +
+            (in_y_origin * input_width * input_depth) +
+            (in_x_origin * input_depth) +
+            in_channel;
+          const int8_t* p_in_1 = p_in_0 + (input_width * input_depth);
+          const int8_t* p_in_2 = p_in_1 + (input_width * input_depth);
+          if (!padding_required) {
+            vld_b_sp_xx(v15, p_in_0, input_depth);
+            vld_b_sp_xx(v16, p_in_0, input_depth);
+            vld_b_sp_xx(v17, p_in_0, input_depth);
+            vld_b_sp_xx(v18, p_in_1, input_depth);
+            vld_b_sp_xx(v19, p_in_1, input_depth);
+            vld_b_sp_xx(v20, p_in_1, input_depth);
+            vld_b_sp_xx(v21, p_in_2, input_depth);
+            vld_b_sp_xx(v22, p_in_2, input_depth);
+            vld_b_sp_xx(v23, p_in_2, input_depth);
+          } else {
+            // Top row
+            if (top_pad || left_pad) {
+              vdup_b_x(v15, -input_offset);
+            } else {
+              vld_b_x(v15, p_in_0);
+            }
+            if (top_pad) {
+              vdup_b_x(v16, -input_offset);
+            } else {
+              vld_b_x(v16, p_in_0 + input_depth);
+            }
+            if (top_pad || right_pad) {
+              vdup_b_x(v17, -input_offset);
+            } else {
+              vld_b_x(v17, p_in_0 + (2 * input_depth));
+            }
+            // Middle row
+            if (left_pad) {
+              vdup_b_x(v18, -input_offset);
+            } else {
+              vld_b_x(v18, p_in_1);
+            }
+            vld_b_x(v19, p_in_1 + input_depth);
+            if (right_pad) {
+              vdup_b_x(v20, -input_offset);
+            } else {
+              vld_b_x(v20, p_in_1 + (2 * input_depth));
+            }
+            // Bottom row
+            if (bottom_pad || left_pad) {
+              vdup_b_x(v21, -input_offset);
+            } else {
+              vld_b_x(v21, p_in_2);
+            }
+            if (bottom_pad) {
+              vdup_b_x(v22, -input_offset);
+            } else {
+              vld_b_x(v22, p_in_2 + input_depth);
+            }
+            if (bottom_pad || right_pad) {
+              vdup_b_x(v23, -input_offset);
+            } else {
+              vld_b_x(v23, p_in_2 + (2 * input_depth));
+            }
+          }
+
+          adwinit_v(v48, v48);
+          adwconv_vxv(v48, v15, cmds, v6);
+          adwconv_vxv(v48, v18, cmds, v9);
+          vdwconv_vxv(v48, v21, cmds, v12);
+
+          vdmulh_w_rn_vv_m(v48, v48, v56);
+          vsha_w_r_vv_m(v48, v48, v60);
+          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, p_output + (out_x * output_depth) + y_offset);
+        }
+      }
+    }
+  }
+}
 
 // special case of input depth = 32n, filter shape of 5x5, stride == 1
 void DepthwiseConvS85x5D32_Stride1(
@@ -628,6 +794,8 @@
   // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro.
   const int stride_width = params.stride_width;
   const int stride_height = params.stride_height;
+  const int pad_width = params.padding_values.width;
+  const int pad_height = params.padding_values.height;
   const int filter_height = filter_shape.Dims(1);
   const int filter_width = filter_shape.Dims(2);
   const int dilation_width_factor = params.dilation_width_factor;
@@ -661,6 +829,8 @@
         } else {
           fn = DepthwiseConvS85x5D32;
         }
+      } else if (filter_width == 3 && filter_height == 3 && pad_width <= 1 && pad_height <= 1) {
+        fn = DepthwiseConvS83x3D32;
       } else {
         fn = DepthwiseConvS8D32;
       }