JSNPE: A Digital Superconducting Spiking Neural Processing Element
文献类型：期刊论文
| 作者 | Jiang, Huilong1,4; Wang, Zhongqi1; Huang, Junying1,3; Chen, Gang2; Zhang, Zhimin1,3; Ye, Xiaochun1,3; Fan, Dongrui1,3; You, Lixing4 |
| 刊名 | IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
 |
| 出版日期 | 2025-06-01 |
| 卷号 | 35期号:4页码:12 |
| 关键词 | Neurons Synapses Superconducting transmission lines Superconducting logic circuits Neuromorphics Magnetic flux Josephson junctions SQUIDs Membrane potentials Logic Josephson junction (JJ) neuron spiking neural network (SNN) superconducting electronics synapse |
| ISSN号 | 1051-8223 |
| DOI | 10.1109/TASC.2025.3561667 |
| 英文摘要 | Superconducting electronic devices are considered a compelling candidate for designing neuromorphic hardware due to their similar behavioral characteristics. Previous works have shown high potential for applying them to design spiking neural networks. However, convincing schemes remain lacking. This article proposes a Josephson junction (JJ)-based spiking neural processing element, JSNPE, a digital scheme composed of a neural and synaptic module. The neuron is a concise quasidigital circuit with three JJs. It operates asynchronously and realizes the integrate-and-fire model. The synapse is a digital circuit constructed with superconducting rapid single flux quantum devices. Compared to previous designs, JSNPE facilitates network scalability, exhibits higher computational resolution, and enables easier weight configuration. To validate the design, we conducted inference tasks on the IRIS and MNIST datasets using WRspice simulation, with inference parameters obtained through software-based training. The analysis indicates that the inference network can achieve a classification accuracy of 96.7% on IRIS, while for MNIST, the classification accuracy for digit "0" reached 94%. We also evaluated the performance and energy efficiency of JSNPE, demonstrating that JSNPE could achieve significant improvements in both metrics compared to complementary metal oxide semiconductor designs. |
| 资助项目 | National Natural Science Foundation of China[62302477] ; CAS Project for the Youth Innovation Promotion Association |
| WOS研究方向 | Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001487985100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42387]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Huang, Junying |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Natl Key Lab Mat Integrated Circuits, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Huilong,Wang, Zhongqi,Huang, Junying,et al. JSNPE: A Digital Superconducting Spiking Neural Processing Element[J]. IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY,2025,35(4):12. |
| APA | Jiang, Huilong.,Wang, Zhongqi.,Huang, Junying.,Chen, Gang.,Zhang, Zhimin.,...&You, Lixing.(2025).JSNPE: A Digital Superconducting Spiking Neural Processing Element.IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY,35(4),12. |
| MLA | Jiang, Huilong,et al."JSNPE: A Digital Superconducting Spiking Neural Processing Element".IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 35.4(2025):12. |
入库方式： OAI收割
来源：计算技术研究所
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