Efficient embedded deep learning

The popularity of deep learning has risen enormously the last few years and neural networks are being applied in numerous fields. These neural networks often require a significant amount of computing power to obtain good results. This makes it difficult to use them for embedded applications. In this thesis we will explore possibilities to make deep learning efficient for embedded systems. One of these possibilities is neural architecture search (NAS), a way to automatically generate neural networks suited for a certain purpose.