Machine learning applications have become widespread over many technological fields. Their application ranges from pattern recognition, like image classification or data mining techniques, to complex human interaction application, like autonomous driving, natural language processing or robotics. A large class of machine learning algorithms are deep neural networks. They are composed by a cascade of several non-linear layers, such as convolutional, activation, down-sampling, and classification layers. The intensive amount of data processing needed to perform inference makes neural network processing not suitable to run over standard processor architectures. This have motivated the research to develop ASIC accelerators that, with a high-parallelism spatial architecture, are able to process multiple data in a more efficient way, enabling neural network processing beside general purpose computing systems.