The increasing amount of data available nowadays has made the use of automatic learning algorithms, also known as machine learning, spread. Machine learning is widely used for applications like speech recognition, natural language processing or robotics. The most popular technique used for these purposes are neural networks. These models require a great amount of computation capacity and until now, GPUs have mainly covered these computations. Recently, field programmable gate arrays (FPGAs) are becoming more common within these applications. The main difference between GPUs and FPGAs is that the latter offer the user the possibility of designing specific hardware instead of using a fixed architecture.