Sigmoid function generator using stochastic adiabatic superconductor logic

Abstract

Stochastic-computing-based artificial neural networks (SC-ANNs) can be used to perform hardware- and energy-efficient neuromorphic computing. We have been developing SC-ANNs using an energy-efficient superconductor logic family, namely, adiabatic quantum-flux-parametron (AQFP) logic. AQFP logic is suitable as a building block for SC-ANNs since it can perform stochastic operations with extremely small energy dissipation. In this Letter, we propose and demonstrate a sigmoid function generator (SFG) for AQFP SC-ANNs, which we refer to as the AQFP SFG. An SFG is an important circuit in neural networks that generates outputs from the sum of weighted inputs in accordance with the sigmoid function. The AQFP SFG performs the sigmoid function using a finite state machine based on an AQFP buffer coupled to a flux storage loop. We experimentally demonstrate that the AQFP SFG generates output signals from stochastic bitstreams in accordance with the sigmoid function and that the characteristics of the sigmoid function can be controlled by a bias current. Furthermore, we show that the AQFP SFG operates with small power dissipation due to its simple structure.