Toward Superconducting Neuromorphic Computing Using Single-Flux-Quantum Circuits

doi:10.1109/TASC.2025.3544687

	Toward Superconducting Neuromorphic Computing Using Single-Flux-Quantum Circuits
	Liu, Zeshi; Chen, Shuo; Qu, Peiyao; Tang, Guangming; You, Haihang
	2025-05-01
发表期刊	IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
ISSN	1051-8223
卷号	35 期号:3 页码:14
摘要	Current artificial intelligence faces challenges in improving computational efficiency due to increasing scale and complexity. Superconducting circuit, as one of the most promising technologies in the post-Moore era, offers ultrahigh-speed computation and ultralow power consumption. Superconducting circuits are driven by pulses, which enables direct execution of pulse-based neuromorphic computing. Consequently, superconducting circuits hold the potential to facilitate higher efficiency and larger scale neuromorphic chips. However, existing efforts neglect the limitations and constraints of superconducting circuits, such as the extra overhead of pulse-based logic, the lack of superconducting memory, and low integration. Hence, their work cannot be utilized in fabricating real superconducting neuromorphic chips. This article introduces superconducting spiking neural network (SSNN), which aims to enable full neuromorphic computing on superconducting circuits. The design of SSNN addresses key issues including a superconducting circuit-based neuron model, weight processing methods suitable for superconducting pulses, and superconducting neuromorphic on-chip networks. SSNN enables complete neuromorphic computing on superconducting circuits. We validate the feasibility and accuracy of SSNN using a standard cell library of superconducting circuits and successfully fabricate the world's first superconducting neuromorphic chip. Our evaluation demonstrates a remarkable 50 x increase in power efficiency compared to state-of-the-art semiconductor designs.
关键词	Circuits Superconducting transmission lines Superconducting logic circuits Superconducting integrated circuits Neuromorphic engineering Neurons Computational modeling Integrated circuit modeling Artificial intelligence Logic Neuromorphic computing single-flux-quantum (SFQ) spiking neural network (SNN) superconducting computing superconducting integrated circuit
DOI	10.1109/TASC.2025.3544687
收录类别	SCI
语种	英语
资助项目	China Postdoctoral Science Foundation[GZB20240760]
WOS研究方向	Engineering ; Physics
WOS类目	Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:001447504200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40687
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tang, Guangming; You, Haihang
作者单位	Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Zeshi,Chen, Shuo,Qu, Peiyao,et al. Toward Superconducting Neuromorphic Computing Using Single-Flux-Quantum Circuits[J]. IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY,2025,35(3):14.
APA	Liu, Zeshi,Chen, Shuo,Qu, Peiyao,Tang, Guangming,&You, Haihang.(2025).Toward Superconducting Neuromorphic Computing Using Single-Flux-Quantum Circuits.IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY,35(3),14.
MLA	Liu, Zeshi,et al."Toward Superconducting Neuromorphic Computing Using Single-Flux-Quantum Circuits".IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 35.3(2025):14.