SupeRBNN: Randomized Binary Neural Network Using Adiabatic
  Superconductor Josephson Devices
Zhengang Li Geng Yuan Tomoharu Yamauchi Zabihi Masoud Yanyue Xie Peiyan Dong Xulong Tang Nobuyuki Yoshikawa Devesh Tiwari Yanzhi Wang Olivia Chen
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with
extremely high energy efficiency. By employing the distinct polarity of current
to denote logic `0' and `1', AQFP devices serve as excellent carriers for
binary neural network (BNN) computations. Although recent research has made
initial strides toward developing an AQFP-based BNN accelerator, several
critical challenges remain, preventing the design from being a comprehensive
solution. In this paper, we propose SupeRBNN, an AQFP-based randomized BNN
acceleration framework that leverages software-hardware co-optimization to
eventually make the AQFP devices a feasible solution for BNN acceleration.
Specifically, we investigate the randomized behavior of the AQFP devices and
analyze the impact of crossbar size on current attenuation, subsequently
formulating the current amplitude into the values suitable for use in BNN
computation. To tackle the accumulation problem and improve overall hardware
performance, we propose a stochastic computing-based accumulation module and a
clocking scheme adjustment-based circuit optimization method. We validate our
SupeRBNN framework across various datasets and network architectures, comparing
it with implementations based on different technologies, including CMOS, ReRAM,
and superconducting RSFQ/ERSFQ. Experimental results demonstrate that our
design achieves an energy efficiency of approximately 7.8x10^4 times higher
than that of the ReRAM-based BNN framework while maintaining a similar level of
model accuracy. Furthermore, when compared with superconductor-based
counterparts, our framework demonstrates at least two orders of magnitude
higher energy efficiency.