Superconductor Computing for Neural Networks

Koki Ishida, Ilkwon Byun, Ikki Nagaoka, Kosuke Fukumitsu, Masamitsu Tanaka, Satoshi Kawakami, Teruo Tanimoto, Takatsugu Ono, Jangwoo Kim, Koji Inoue

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The superconductor single-flux-quantum (SFQ) logic family has been recognized as a promising solution for the post-Moore era, thanks to the ultrafast and low-power switching characteristics of superconductor devices. Researchers have made tremendous efforts in various aspects, especially in device and circuit design. However, there has been little progress in designing a convincing SFQ-based architectural unit due to a lack of understanding about its potentials and limitations at the architectural level. This article provides the design principles for SFQ-based architectural units with an extremely high-performance neural processing unit (NPU). To achieve our goal, we developed and validated a simulation framework to identify critical architectural bottlenecks in designing a performance-effective SFQ-based NPU. We propose SuperNPU, which outperforms a conventional state-of-the-art NPU by 23 times in terms of computing performance and 1.23 times in power efficiency even with the cooling cost of the 4K environment.

Original languageEnglish
Article number9395193
Pages (from-to)19-26
Number of pages8
JournalIEEE Micro
Volume41
Issue number3
DOIs
Publication statusPublished - May 1 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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