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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728164601
DOIs
StatePublished - Jun 2020
Event2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 - Honolulu, United States
Duration: 16 Jun 202019 Jun 2020

Publication series

NameDigest of Technical Papers - Symposium on VLSI Technology
Volume2020-June
ISSN (Print)0743-1562

Conference

Conference2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020
Country/TerritoryUnited States
CityHonolulu
Period16/06/2019/06/20

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