Hardware Convolutional Neural Network based on Residue Number System The prevailing numeric system in arithmetic circuits hardware implementations is unquestionably the Binary Number System (BNS). Nonetheless, over the last few years, an ever-greater shift towards parallel architectures is highlighting the limits of this arithmetic representation. In particular, being it a positional representation, the carry chains processing time constitutes the strongest limitation to the parallelism exploitation. Representing numbers with unconventional number systems seems to be one of the most promising options to overcome this limit. Among the unconventional number systems proposed in literature, the Residue Number System (RNS) is one of the representations with the greatest potential.