Convolutional Neural Networks (CNNs) are a particular kind of Neural Network (NNs) that compute the outputs by means of a convolution operation between a set of 3D inputs and a 4D tensor filter. They find application in many fields such as image processing and classification, speech recognition and object detection but their high prediction accuracy comes at the cost of high computation and memory demand and a long inference time. Many attempts have been made at identifying the best hardware support and at researching strategies to concurrently accelerate the inference of CNNs while also limiting hardware complexity and improving flexibility. FPGAs have lately been the platform of choice because they offer a good compromise between flexibility and energy efficiency; quantization has shown to reduce computation complexity while maintaining an acceptable accuracy; loop unrolling can increase the computation parallelization and speed up inference; and the number of required multiplications can be reduced thanks to computational transforms such as the Winograd Algorithm, that is able to reduce the multiplication demands of up to 4x with filters of size (4x4) or lower.