Design and Implementation of Energy-Efficient Binary Neural Networks Using Adiabatic Quantum-Flux-Parametron Logic

Abstract

Adiabatic quantum-flux-parametron (AQFP) logic is a promising technology for future energy-efficient, highperformance information processing systems. It has significantly low power dissipation thanks to the adiabatic switching of Josephson junctions. In this paper, we introduce an AQFPbased binary neural network (BNN) design methodology utilizing an in-memory computing scheme, analog accumulation, and a crossbar structure. The proposed design can effectively resolve the memory issue in superconducting digital circuits by significantly reducing memory usage in a non-Von Neumann fashion compared to conventional neural networks. As a proof-ofconcept, we designed and implemented an 8×8 AQFP BNN using the proposed design methodology targeted the AIST 10kA/cm2 4-layer niobium process. The Josephson junction count and energy dissipation of the proposed 8×8 BNN design are 2236 and 11.18 aJ, respectively.