diff --git a/src/tim/vx/ops/addn_test.cc b/src/tim/vx/ops/addn_test.cc
new file mode 100644
index 0000000..9ac2b50
--- /dev/null
+++ b/src/tim/vx/ops/addn_test.cc
@@ -0,0 +1,168 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops/addn.h"
+#include "tim/vx/types.h"
+#include "src/tim/vx/test_utils.h"
+
+#include "gtest/gtest.h"
+
+TEST(AddN, shape_2_2_int32) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType io_shape({2, 2});
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
+                            io_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
+                            io_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec);
+    auto input_tensor_y = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<int32_t> in_data_x = { 
+        3, 5, 
+        4, 8 };  
+    std::vector<int32_t> in_data_y = { 
+        1, 6, 
+        2, 9 };  
+    std::vector<int32_t> golden = { 
+        4, 11,
+        6, 17 };  //correct answer
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
+    auto op = graph->CreateOperation<tim::vx::ops::AddN>(2);   //To refer to the AddN function definition to give the parameters
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<int32_t> output(4);
+    
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
+TEST(AddN, shape_3_1_float32) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType io_shape({3, 1});    
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
+                            io_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
+                            io_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec);
+    auto input_tensor_y = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data_x = { 
+        3, 5, 7 };  
+    std::vector<float> in_data_y = { 
+        1, 6, 2 };  
+    std::vector<float> golden = { 
+        4, 11, 9 };  
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
+    auto op = graph->CreateOperation<tim::vx::ops::AddN>(2);   
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<float> output(3);
+    
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
+TEST(AddN, shape_2_2_uint8_QuantizedTest) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType in_shape({2, 2}); 
+    tim::vx::ShapeType out_shape({2, 2});
+
+    float InputMin = -127, InputMax = 128, OutputMin = -127, OutputMax = 128;
+
+    std::pair<float, int32_t> scalesAndZp;     
+
+    scalesAndZp = QuantizationParams<u_int8_t>(InputMin, InputMax);
+    std::vector<float> scalesInput = {scalesAndZp.first};   //scale
+    std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
+
+    scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
+    std::vector<float> scalesOutput = {scalesAndZp.first};
+    std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
+
+
+    tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
+                                   scalesInput, zeroPointsInput);
+    tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
+                                    scalesOutput, zeroPointsOutput);                
+
+    tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8, in_shape,
+                                 tim::vx::TensorAttribute::INPUT, quantInput);
+    tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8, in_shape,
+                                 tim::vx::TensorAttribute::INPUT, quantInput);
+
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
+                                tim::vx::TensorAttribute::OUTPUT, quantOutput);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec_x);
+    auto input_tensor_y = graph->CreateTensor(input_spec_y);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_float_data_x = { 
+        3.1, 5.1, 
+        4.1, 8 };  
+    std::vector<float> in_float_data_y = { 
+        1.1, 6.1, 
+        2.1, 9 }; 
+    std::vector<float> golden_float = { 
+        4.2, 11.2,
+        6.2, 17 };  
+
+    std::vector<u_int8_t> input_data_x =
+      Quantize<uint8_t>(in_float_data_x, scalesInput[0], zeroPointsInput[0]);//Quantification process
+    std::vector<u_int8_t> input_data_y =
+      Quantize<uint8_t>(in_float_data_y, scalesInput[0], zeroPointsInput[0]);
+    std::vector<u_int8_t> golden =
+      Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(input_data_x.data(), input_data_x.size()*4));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(input_data_y.data(), input_data_y.size()*4));
+    auto op = graph->CreateOperation<tim::vx::ops::AddN>(2);   
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<uint8_t> output(4);
+    
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
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