diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD
index cdaf83d..9957e7a 100644
--- a/tflm/opt/BUILD
+++ b/tflm/opt/BUILD
@@ -35,6 +35,7 @@
         "leaky_relu_s16.cc",
         "leaky_relu_s8.cc",
         "logistic_s8.cc",
+        "max_pool_s16.cc",
         "max_pool_s8.cc",
         "memcpy.cc",
         "resize_nearest_neighbor_s8.cc",
diff --git a/tflm/opt/max_pool_s16.cc b/tflm/opt/max_pool_s16.cc
new file mode 100644
index 0000000..4e3aa46
--- /dev/null
+++ b/tflm/opt/max_pool_s16.cc
@@ -0,0 +1,100 @@
+/*
+ * Copyright 2024 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 "crt/kelvin.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/runtime_shape.h"
+#include "tensorflow/lite/kernels/internal/types.h"
+
+namespace kelvin::opt {
+void MaxPoolS16(const tflite::PoolParams &params,
+                const tflite::RuntimeShape &input_shape,
+                const int16_t *input_data,
+                const tflite::RuntimeShape &output_shape,
+                int16_t *output_data) {
+  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+  const int depth = MatchingDim(input_shape, 3, output_shape, 3);
+  const int input_height = input_shape.Dims(1);
+  const int input_width = input_shape.Dims(2);
+  const int output_height = output_shape.Dims(1);
+  const int output_width = output_shape.Dims(2);
+  const int stride_height = params.stride_height;
+  const int stride_width = params.stride_width;
+  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) - params.padding_values.width;
+        const int in_y_origin =
+            (out_y * stride_height) - params.padding_values.height;
+
+        // Compute the boundaries of the filter region clamped so as to
+        // ensure that the filter window fits in the input array.
+        const int filter_x_start = std::max(0, -in_x_origin);
+        const int filter_x_end =
+            std::min(params.filter_width, input_width - in_x_origin);
+        const int filter_y_start = std::max(0, -in_y_origin);
+        const int filter_y_end =
+            std::min(params.filter_height, input_height - in_y_origin);
+
+        int channel = 0;
+        for (; channel + 16 <= depth; channel += 16) {
+          vdup_h_x(v0, params.quantized_activation_min);
+          for (int filter_y = filter_y_start; filter_y < filter_y_end;
+               ++filter_y) {
+            for (int filter_x = filter_x_start; filter_x < filter_x_end;
+                 ++filter_x) {
+              const int in_x = in_x_origin + filter_x;
+              const int in_y = in_y_origin + filter_y;
+              const int16_t *local_input =
+                  input_data + Offset(input_shape, batch, in_y, in_x, channel);
+              vld_h_x(v1, local_input);
+              vmax_h_vv(v0, v0, v1);
+            }
+          }
+          vmin_h_vx(v0, v0, params.quantized_activation_max);
+          int16_t *local_output =
+              output_data + Offset(output_shape, batch, out_y, out_x, channel);
+          vst_h_x(v0, local_output);
+        }
+
+        if (channel == depth) {
+          continue;
+        }
+        int remaining_channels = depth - channel;
+        vdup_h_x(v0, params.quantized_activation_min);
+        for (int filter_y = filter_y_start; filter_y < filter_y_end;
+             ++filter_y) {
+          for (int filter_x = filter_x_start; filter_x < filter_x_end;
+               ++filter_x) {
+            const int in_x = in_x_origin + filter_x;
+            const int in_y = in_y_origin + filter_y;
+            const int16_t *local_input =
+                input_data + Offset(input_shape, batch, in_y, in_x, depth - 1);
+            vld_h_l_xx(v1, local_input, remaining_channels);
+            vmax_h_vv(v0, v0, v1);
+          }
+        }
+        vmin_h_vx(v0, v0, params.quantized_activation_max);
+        int16_t *local_output =
+            output_data + Offset(output_shape, batch, out_y, out_x, depth - 1);
+        vst_h_l_xx(v0, local_output, remaining_channels);
+      }
+    }
+  }
+}
+
+}  // namespace kelvin::opt
diff --git a/tflm/opt/opt.h b/tflm/opt/opt.h
index 85f5019..053fbd6 100644
--- a/tflm/opt/opt.h
+++ b/tflm/opt/opt.h
@@ -97,6 +97,10 @@
                const tflite::RuntimeShape& input_shape,
                const int8_t* input_data,
                const tflite::RuntimeShape& output_shape, int8_t* output_data);
+void MaxPoolS16(const tflite::PoolParams& params,
+                const tflite::RuntimeShape& input_shape,
+                const int16_t* input_data,
+                const tflite::RuntimeShape& output_shape, int16_t* output_data);
 void MulS8(const tflite::ArithmeticParams& params,
            const tflite::RuntimeShape& input1_shape, const int8_t* input1_data,
            const tflite::RuntimeShape& input2_shape, const int8_t* input2_data,