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diff --git a/onert-micro/luci-interpreter/pal/common/PALAveragePool2DCommon.h b/onert-micro/luci-interpreter/pal/common/PALAveragePool2DCommon.h
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+++ b/onert-micro/luci-interpreter/pal/common/PALAveragePool2DCommon.h
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+ *
+ * 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.
+ */
+
+#ifndef LUCI_INTERPRETER_PAL_AVERAGE_POOL_2D_COMMON_H
+#define LUCI_INTERPRETER_PAL_AVERAGE_POOL_2D_COMMON_H
+
+#include "Params.h"
+#include "PALUtils.h"
+
+namespace luci_interpreter_pal
+{
+
+// TODO: reduce code duplication with MaxPool
+inline void AveragePool(const PoolParams &params, const luci_interpreter::RuntimeShape &input_shape,
+                        const float *input_data, const luci_interpreter::RuntimeShape &output_shape,
+                        float *output_data)
+{
+  const int batches = input_shape.dims(0);
+  const int depth = output_shape.dims(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)
+      {
+        for (int channel = 0; channel < depth; ++channel)
+        {
+          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);
+
+          float total = 0.f;
+          float filter_count = 0;
+
+          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 int input_data_offset =
+                ((batch * input_shape.dims(1) + in_y) * input_shape.dims(2) + in_x) *
+                  input_shape.dims(3) +
+                channel;
+
+              total += input_data[input_data_offset];
+              filter_count++;
+            }
+          }
+          const int output_data_offset =
+            ((batch * output_shape.dims(1) + out_y) * output_shape.dims(2) + out_x) *
+              output_shape.dims(3) +
+            channel;
+
+          assert(filter_count != 0);
+          const float average = total / filter_count;
+
+          output_data[output_data_offset] =
+            std::min(std::max(average, params.float_activation_min), params.float_activation_max);
+        }
+      }
+    }
+  }
+}
+} // namespace luci_interpreter_pal
+
+#endif // LUCI_INTERPRETER_PAL_AVERAGE_POOL_2D_COMMON_H