Artificial intelligence is moving ahead at a staggering speed in applications and is spreading rapidly in many aspects of daily life such as face and gesture recognition, vision, autonomous cars, remote sensing and robots, agriculture, augmented reality, and bio-metrics, just to name a few. The potential is even greater since modern approaches of artificial intelligence, such as Machine Learning or Deep Learning, can be applied onto smaller devices such as smartphones or even smaller ones like embedded systems with severe performance constraints. One of the main problems of these new approach to artificial intelligence is the resource usage. Convolutional Neural Networks (CNNs), for instance, need high amounts of data to work, thus implying heavyweight computations during the training phase, as well as during inference stages.