Machine learning has been very successful in many applications. These applications range from image classification, machine translation, autonomous driving, virtual personal assistant to medical services. Deep neural networks are algorithms used in machine learning that runs data through multiple layers such as convolution, activation, pooling, and classification. There is a huge demand for deep learning applications on embedded devices. However, these layers, particularly the convolutions, require a massive amount of computation, which is one of primary consideration, especially for resource constraint devices. Due to the different hardware platforms and neural network libraries, it is essential to have a suitable methodology that could select the best neural network architecture for different application requirements.