Nowadays, the main challenges in embedded machine learning are related to artificial neural networks. Inspired by the biological neural networks, artificial neural networks are able to solve complex problems, by performing a tremendous amount of relatively simple parallel computations. Embedding such networks in autonomous devices raises the issues of energy efficiency, resource usage and accuracy. The aim of this paper is to provide a comprehensive analysis of the efforts made in recent years to implement artificial neural network architectures suitable for embedded applications.