Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network
2020-12
会议录名称2020 IEEE SYMPOSIUM ON VLSI TECHNOLOGY
发表状态已发表
DOI10.1109/VLSITechnology18217.2020.9265033
摘要

A unique compact Poisson neuron that encodes information in the tunable duty cycle of probabilistic spike trains is presented as an enabling technology for cost-effective spiking neural network (SNN) hardware. The Poisson neuron exploits the back-hopping oscillation (BHO) in scalable spin-transfer torque (STT)-MRAM. The macrospin LLGS simulation confirms that the coupled local Joule heating and STT effects are responsible for the bias-dependent BHO. The complete neuron circuit design is at least 6× smaller than the state-of-the-art integrate-and- fire (IF) CMOS neuron. Hardware-friendly all-spin deep SNNs achieve equivalent accuracy to deep neural networks (DNN), 98.4 % for MNIST, even when considering the probabilistic nature of neurons.

关键词STT-MRAM Poisson Neuron Spiking Neural Network
会议名称2020 IEEE Symposium on VLSI Technology
会议地点Honolulu, HI, USA, USA
会议日期16-19 June 2020
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收录类别SCI ; CPCI ; CPCI-S ; EI
语种英语
出版者IEEE
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文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/124616
专题信息科学与技术学院
信息科学与技术学院_PI研究组_祝智峰组
通讯作者Tuo-Hung Hou
作者单位
1.Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu, Taiwan
2.National University of Singapore, Singapore; 3ShanghaiTech University, Shanghai, China
3.ShanghaiTech University, Shanghai, China
4.Industrial Technology Research Institute, Chutung, Hsinchu, Taiwan;
5.National Taiwan University, Taipei, Taiwan
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GB/T 7714
Ming-Hung Wu,Ming-Shun Huang,Zhifeng Zhu,et al. Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network[C]:IEEE,2020.
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