2021年第68回応用物理学会春季学術講演会

講演情報

一般セッション(口頭講演)

11 超伝導 » 11.5 接合,回路作製プロセスおよびデジタル応用

[18p-Z27-1~14] 11.5 接合,回路作製プロセスおよびデジタル応用

2021年3月18日(木) 13:30 〜 17:15 Z27 (Z27)

寺井 弘高(情通機構)、山梨 裕希(横国大)

14:45 〜 15:00

[18p-Z27-6] Design and evaluation of an AQFP/RSFQ sigmoid-function generator for neural networks based on stochastic computing

〇(M2)Wenhui Luo1、Naoki Takeuchi2、Olivia Chen2、Nobuyuki Yoshikawa1,2 (1.Yokohama Natl. Univ.、2.IAS, Yokohama Natl. Univ.)

キーワード:superconductor, stochastic computing, sigmoid-function

We propose a sigmoid-function generator (SFG) using adiabatic quantum-flux-parametron (AQFP) circuit and rapid single-flux-quantum (RSFQ) circuit for hardware implementation of deep neural networks based on stochastic computing. The SFG corresponds to an AQFP/RSFQ interface and a superconducting loop performed as a counter. The flux quantum can be stored in loop inductance and the polarity of loop current depends on the logic in random bit stream from the former AQFP circuit. Finally, an AQFP buffer chain is used to read out the polarity to perform sigmoid-function. We designed and fabricated a sigmoid-function generator using AIST high-speed standard process (HSTP) and demonstrated the generation at 100kHz.