Design of Discrete Hopfield Neural Network Using a Single Flux Quantum Circuit

The superconductor single flux quantum (SFQ) logic family has been recognized as a promising candidate to resolve the energy consumption crisis in the post-Moore era, owing to its high switching speed and low power consumption. In the field of machine learning, where technology and computational requirements are growing rapidly (e.g., image recognition and natural language processing), there is great potential for the implementation of SFQ circuits. In this study, we investigate and implement a discrete Hopfield neural network (DHNN) using SFQ circuits. A DHNN is a binary neural network with less information than a standard full precision neural network; it also provides a higher processing speed. It is mainly used for pattern recognition and recovery. We designed the DHNN circuit with two patterns, each with eight elements. The circuit operates at the clock frequency of more than 50 GHz.