Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network

Ming Hung Wu, Ming Chun Hong, Chih Cheng Chang, Paritosh Sahu, Jeng Hua Wei, Heng Yuan Lee, Shyh Shyuan Shcu, Tuo-Hung Hou

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

6 Scopus citations

Abstract

This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training.

Original languageEnglish
Title of host publication2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesT34-T35
Number of pages2
ISBN (Electronic)9784863487178
DOIs
StatePublished - Jun 2019
Event39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan
Duration: 9 Jun 201914 Jun 2019

Publication series

NameDigest of Technical Papers - Symposium on VLSI Technology
Volume2019-June
ISSN (Print)0743-1562

Conference

Conference39th Symposium on VLSI Technology, VLSI Technology 2019
CountryJapan
CityKyoto
Period9/06/1914/06/19

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