diff --git a/tests/use_case/kws/InferenceTestDSCNN.cc b/tests/use_case/kws/InferenceTestDSCNN.cc
new file mode 100644
index 0000000..06358a4
--- /dev/null
+++ b/tests/use_case/kws/InferenceTestDSCNN.cc
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * 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 "DsCnnModel.hpp"
+#include "hal.h"
+#include "TestData_kws.hpp"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <catch.hpp>
+#include <random>
+
+bool RunInference(arm::app::Model& model, const int8_t vec[])
+{
+    TfLiteTensor* inputTensor = model.GetInputTensor(0);
+    REQUIRE(inputTensor);
+
+    const size_t copySz = inputTensor->bytes < IFM_DATA_SIZE ?
+                            inputTensor->bytes :
+                            IFM_DATA_SIZE;
+    memcpy(inputTensor->data.data, vec, copySz);
+
+    return model.RunInference();
+}
+
+bool RunInferenceRandom(arm::app::Model& model)
+{
+    TfLiteTensor* inputTensor = model.GetInputTensor(0);
+    REQUIRE(inputTensor);
+
+    std::random_device rndDevice;
+    std::mt19937 mersenneGen{rndDevice()};
+    std::uniform_int_distribution<short> dist {-128, 127};
+
+    auto gen = [&dist, &mersenneGen](){
+                   return dist(mersenneGen);
+               };
+
+    std::vector<int8_t> randomAudio(inputTensor->bytes);
+    std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
+
+    REQUIRE(RunInference(model, randomAudio.data()));
+    return true;
+}
+
+template<typename T>
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
+{
+    REQUIRE(RunInference(model, input_goldenFV));
+
+    TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
+    REQUIRE(outputTensor);
+    REQUIRE(outputTensor->bytes == OFM_DATA_SIZE);
+    auto tensorData = tflite::GetTensorData<T>(outputTensor);
+    REQUIRE(tensorData);
+
+    for (size_t i = 0; i < outputTensor->bytes; i++) {
+        REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
+    }
+}
+
+TEST_CASE("Running random inference with TensorFlow Lite Micro and DsCnnModel Int8", "[DS_CNN]")
+{
+    arm::app::DsCnnModel model{};
+
+    REQUIRE_FALSE(model.IsInited());
+    REQUIRE(model.Init());
+    REQUIRE(model.IsInited());
+
+    REQUIRE(RunInferenceRandom(model));
+}
+
+TEST_CASE("Running inference with TensorFlow Lite Micro and DsCnnModel Uint8", "[DS_CNN]")
+{
+    for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+        const int8_t* input_goldenFV = get_ifm_data_array(i);;
+        const int8_t* output_goldenFV = get_ofm_data_array(i);
+
+        DYNAMIC_SECTION("Executing inference with re-init")
+        {
+            arm::app::DsCnnModel model{};
+
+            REQUIRE_FALSE(model.IsInited());
+            REQUIRE(model.Init());
+            REQUIRE(model.IsInited());
+
+            TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
+
+        }
+    }
+}