Image classification and detection tasks have been approached in numerous ways throughout the years. Traditional approaches used conventional machine learning techniques dealing with hand-crafted vectors of features which had to be carefully extracted from the images with considerable domain-expertise. Deep learning algorithms have overcome this limitation by providing architectures able to work on raw data while outperforming traditional techniques. A deep learning architecture which proved to be very successful in the context of computer vision is the Convolutional Neural Network, an architecture able to achieve previously unreachable performances, making it, nowadays, a de-facto standard in classification and detection tasks. However, a drawback of deep learning architectures is their high computational footprint which has limited, until recently, their adoption only to high-performance servers and workstations.