Spin orbit torque device based stochastic multi-bit synapses for on-chip STDP learning

Gyuseong Kang, Yunho Jang, Jongsun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As a large number of neurons and synapses are needed in spike neural network (SNN) design, emerging devices have been employed to implement synapses and neurons. In this paper, we present a stochastic multi-bit spin orbit torque (SOT) memory based synapse, where only one SOT device is switched for potentiation and depression using modified Gray code. The modified Gray code based approach needs only N devices to represent 2N levels of synapse weights. Early read termination scheme is also adopted to reduce the power consumption of training process by turning off less associated neurons and its ADCs. For MNIST dataset, with comparable classification accuracy, the proposed SNN architecture using 3-bit synapse achieves 68.7% reduction of ADC overhead compared to the conventional 8-level synapse.

Original languageEnglish
Title of host publicationISLPED 2018 - Proceedings of the 2018 International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357043
DOIs
Publication statusPublished - 2018 Jul 23
Event23rd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2018 - Bellevue, United States
Duration: 2018 Jul 232018 Jul 25

Other

Other23rd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2018
CountryUnited States
CityBellevue
Period18/7/2318/7/25

Keywords

  • Neuromorphic processor
  • Spiking neural network
  • Spin orbit torque
  • STDP
  • Stochastic synapse

ASJC Scopus subject areas

  • Engineering(all)

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    Kang, G., Jang, Y., & Park, J. (2018). Spin orbit torque device based stochastic multi-bit synapses for on-chip STDP learning. In ISLPED 2018 - Proceedings of the 2018 International Symposium on Low Power Electronics and Design [a21] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3218603.3218654