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


The superconductor single-flux-quantum (SFQ) logic family has been recognized as a promising solution for the post-Moore era, thanks to the ultra-fast 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 4 K environment.

Original languageEnglish
JournalIEEE Micro
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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