Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network

Ming Hung Wu, Ming Shun Huang, Zhifeng Zhu, Fu Xiang Liang, Ming Chun Hong, Jiefang Deng, Jeng Hua Wei, Shyh Shyuan Sheu, Chih I. Wu, Gengchiau Liang, Tuo Hung Hou

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728164601
DOIs
出版狀態Published - 6月 2020
事件2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 - Honolulu, United States
持續時間: 16 6月 202019 6月 2020

出版系列

名字Digest of Technical Papers - Symposium on VLSI Technology
2020-June
ISSN(列印)0743-1562

Conference

Conference2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020
國家/地區United States
城市Honolulu
期間16/06/2019/06/20

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