diff --git a/mace/dsp/test/quantized_add_test.cc b/mace/dsp/test/quantized_add_test.cc
new file mode 100644
index 0000000000000000000000000000000000000000..3d89f45bfa3f8facf9979ac0f61a11c27c44131a
--- /dev/null
+++ b/mace/dsp/test/quantized_add_test.cc
@@ -0,0 +1,186 @@
+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/dsp/hexagon_control_wrapper.h"
+#include "gtest/gtest.h"
+#include <math.h>
+
+using namespace mace;
+
+static NetDef BuildNetDef() {
+  NetDef net;
+  net.set_name("quantized_add_test");
+  // input op
+  OperatorDef *input_op = net.add_op();
+  input_op->set_name("input_node");
+  input_op->set_type("INPUT");
+  input_op->set_node_id(0);
+  input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1000);
+
+  // add op
+  OperatorDef *add_op = net.add_op();
+  add_op->set_name("add");
+  add_op->set_type("QuantizedAdd_8p8to8");
+  add_op->set_node_id(1);
+  add_op->set_padding(0);
+  add_op->add_input("input_node");
+  add_op->add_input("add_num");
+  add_op->add_input("input_min");
+  add_op->add_input("input_max");
+  add_op->add_input("add_num_min");
+  add_op->add_input("add_num_max");
+  add_op->add_output("add:0");
+  add_op->add_output("add:1");
+  add_op->add_output("add:2");
+  NodeInput *input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(0);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(10);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(11);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(12);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(13);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(14);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(15);
+  input_node_input->set_output_port(0);
+  input_node_input = add_op->add_node_input();
+  input_node_input->set_node_id(16);
+  input_node_input->set_output_port(0);
+
+  add_op->add_out_max_byte_size(1000);
+  add_op->add_out_max_byte_size(1000);
+  add_op->add_out_max_byte_size(1000);
+
+  // output op
+  OperatorDef *output_op = net.add_op();
+  output_op->set_name("__output__");
+  output_op->set_type("OUTPUT");
+  output_op->set_op_id(2);
+  input_node_input = output_op->add_node_input();
+  input_node_input->set_node_id(1);
+  input_node_input->set_output_port(0);
+
+  // tensor
+  TensorProto *add_num_tensor = net.add_tensors();
+  add_num_tensor->set_name("add_num");
+  add_num_tensor->add_dims(3);
+  add_num_tensor->set_data_type(DataType::DT_UINT8);
+  add_num_tensor->set_node_id(10);
+  add_num_tensor->add_int32_data(0);
+  add_num_tensor->add_int32_data(127);
+  add_num_tensor->add_int32_data(255);
+
+  TensorProto *input_min_tensor = net.add_tensors();
+  input_min_tensor->set_name("input_min");
+  input_min_tensor->add_dims(1);
+  input_min_tensor->set_data_type(DataType::DT_FLOAT);
+  input_min_tensor->set_node_id(11);
+  input_min_tensor->add_float_data(-100);
+
+  TensorProto *input_max_tensor = net.add_tensors();
+  input_max_tensor->set_name("input_max");
+  input_max_tensor->add_dims(1);
+  input_max_tensor->set_data_type(DataType::DT_FLOAT);
+  input_max_tensor->set_node_id(12);
+  input_max_tensor->add_float_data(50.0);
+
+  TensorProto *add_num_min_tensor = net.add_tensors();
+  add_num_min_tensor->set_name("add_num_min");
+  add_num_min_tensor->add_dims(1);
+  add_num_min_tensor->set_data_type(DataType::DT_FLOAT);
+  add_num_min_tensor->set_node_id(13);
+  add_num_min_tensor->add_float_data(0);
+
+  TensorProto *add_num_max_tensor = net.add_tensors();
+  add_num_max_tensor->set_name("add_num_max");
+  add_num_max_tensor->add_dims(1);
+  add_num_max_tensor->set_data_type(DataType::DT_FLOAT);
+  add_num_max_tensor->set_node_id(14);
+  add_num_max_tensor->add_float_data(100.0);
+
+  TensorProto *output_min_tensor = net.add_tensors();
+  output_min_tensor->set_name("output_min");
+  output_min_tensor->add_dims(1);
+  output_min_tensor->set_data_type(DataType::DT_FLOAT);
+  output_min_tensor->set_node_id(15);
+  output_min_tensor->add_float_data(-INFINITY);
+
+  TensorProto *output_max_tensor = net.add_tensors();
+  output_max_tensor->set_name("output_max");
+  output_max_tensor->add_dims(1);
+  output_max_tensor->set_data_type(DataType::DT_FLOAT);
+  output_max_tensor->set_node_id(16);
+  output_max_tensor->add_float_data(INFINITY);
+
+
+  // input & output info
+  InputInfo *input_info = net.add_input_info();
+  input_info->set_name("input_node");
+  input_info->set_node_id(0);
+  input_info->add_dims(1);
+  input_info->add_dims(1);
+  input_info->add_dims(1);
+  input_info->add_dims(3);
+  input_info->set_data_type(DataType::DT_UINT8);
+  input_info->set_max_byte_size(1000);
+  OutputInfo *output_info = net.add_output_info();
+  output_info->set_name("output_node");
+  output_info->set_node_id(1);
+  output_info->add_dims(1);
+  output_info->add_dims(1);
+  output_info->add_dims(1);
+  output_info->add_dims(3);
+  output_info->set_data_type(DataType::DT_UINT8);
+  output_info->set_max_byte_size(1000);
+
+  return net;
+}
+
+TEST(QuantizedAddTest, QuantizedAdd) {
+  testing::internal::LogToStderr();
+  HexagonControlWrapper wrapper;
+  wrapper.Init();
+  wrapper.SetDebugLevel(10);
+  wrapper.Config();
+
+  NetDef net = BuildNetDef();
+  wrapper.SetupGraph(net);
+
+  Allocator *cpu_allocator = GetDeviceAllocator(DeviceType::CPU);
+  Tensor input_tensor(cpu_allocator, DT_UINT8);
+  Tensor output_tensor(cpu_allocator, DT_UINT8);
+  input_tensor.Resize({1, 1, 1, 3});
+  output_tensor.Resize({1, 1, 1, 3});
+  uint8_t *input_data = input_tensor.mutable_data<uint8_t>();
+  const uint8_t *output_data = output_tensor.data<uint8_t>();
+  // [-100.0 0 50] + [0.0, 50.0, 100.0] = [-100.0, 50.0, 150.0]
+  // s=0.5859, q0=170, [0, 170, 255]
+  // s=0.3906, q0=0, [0, 127, 255]
+  input_data[0] = 0;
+  input_data[1] = 170;
+  input_data[2] = 250;
+
+  VLOG(0) << wrapper.ExecuteGraph(input_tensor, &output_tensor);
+  wrapper.PrintLog();
+
+  // -120.0~176.47, [17, 146,232]
+  for (int i = 0; i < output_tensor.size(); ++i) {
+    std::cout << (int32_t) output_data[i] << " ";
+  }
+  std::cout << std::endl;
+
+  VLOG(0) << wrapper.TeardownGraph();
+  wrapper.Finalize();
+}
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