Neural networks (NNs) are nowadays widely used for many applications like speech and image recognition. This kind of neural networks has already exceeded human accuracy in many domains at the cost of high complexity from a computational point of view. Usually, CPU and GPU are used to implement such algorithms. GPUs perform better than CPUs thanks to their high number of cores and their wider buses which allow increasing the throughput to the memory. However, while GPUs can reach very high-performances for data-oriented algorithms, their power consumption is very high. For this reason, for low power application, neural network accelerators are designed to obtain high performance with low power consumption.