Modular Arithmetic based Circuits and Systems for Emerging Technologies and Applications: Deep Neural Networks and Cryptography
Energy efficiency and limited power consumption are key aspects for the next-generation of integrated circuits and systems. Thus, together with the increase of performance, they should drive the design of new architectures and arithmetic units. Unconventional number systems, namely Residue Number Systems (RNS), may hold the answer to these emerging challenges. RNS relies on use of modular arithmetic to perform additions, subtractions and multiplications in parallel without any dependency between the RNSdigits. Due to a few limitations such as conversion overheads and division, only recently RNS have experienced a significant number of advances in its application to new domains, such as Deep Convolutional Neural Networks (DCNN) and cryptography. In this seminar we present a state-of-the-art overview concerning the use of the RNS not only to improve the performance of public-key algorithms but to make them more resistant to attacks. RNS for emerging post-quantum algorithms, namely the ones supporting lattice based cryptosystems (LBCs), and Homomorphic Encryption (FHE) are also covered in this seminar. The potential of RNS for matrix multiplication and the application for the high-performance implementation of deep convolutional neural networks (DCNNs) is unveiled.