A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron Superconducting Technology Ruizhe Cai Olivia Chen Ning Liu Ao Ren Yokohama National University Caiwen Ding Northeastern University Japan Northeastern University USA
[email protected] USA {cai.ruiz,ren.ao}@husky.neu.edu {liu.ning,ding.ca}@husky.neu.edu Xuehai Qian Jie Han Wenhui Luo University of Southern California University of Alberta Yokohama National University USA Canada Japan
[email protected] [email protected] [email protected] Nobuyuki Yoshikawa Yanzhi Wang Yokohama National University Northeastern University Japan USA
[email protected] [email protected] ABSTRACT increases the difficulty to avoid RAW hazards; the second is The Adiabatic Quantum-Flux-Parametron (AQFP) supercon- the unique opportunity of true random number generation ducting technology has been recently developed, which achieves (RNG) using a single AQFP buffer, far more efficient than the highest energy efficiency among superconducting logic RNG in CMOS. We point out that these two characteristics families, potentially 104-105 gain compared with state-of-the- make AQFP especially compatible with the stochastic com- art CMOS. In 2016, the successful fabrication and testing of puting (SC) technique, which uses a time-independent bit AQFP-based circuits with the scale of 83,000 JJs have demon- sequence for value representation, and is compatible with strated the scalability and potential of implementing large- the deep pipelining nature. Further, the application of SC scale systems using AQFP. As a result, it will be promising has been investigated in DNNs in prior work, and the suit- for AQFP in high-performance computing and deep space ability has been illustrated as SC is more compatible with applications, with Deep Neural Network (DNN) inference approximate computations.