@inproceedings{289b926bfe504e429d4e552a534c9077,
title = "Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network",
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.",
author = "Wu, {Ming Hung} and Huang, {Ming Shun} and Zhifeng Zhu and Liang, {Fu Xiang} and Hong, {Ming Chun} and Jiefang Deng and Wei, {Jeng Hua} and Sheu, {Shyh Shyuan} and Wu, {Chih I.} and Gengchiau Liang and Hou, {Tuo Hung}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 ; Conference date: 16-06-2020 Through 19-06-2020",
year = "2020",
month = jun,
doi = "10.1109/VLSITechnology18217.2020.9265033",
language = "English",
series = "Digest of Technical Papers - Symposium on VLSI Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Symposium on VLSI Technology, VLSI Technology 2020 - Proceedings",
address = "美國",
}