Abstract

In this work, single event effects (SEEs) are analyzed in SRAM- and RRAM-based neuromorphic computing systems. SPICE simulation is employed to model single event upset (SEU) at the array level. Then SEU effects are mapped to the weight pattern change of a multi-layer perceptron (MLP), a representative artificial neural network for MNIST handwritten digit recognition. Simulations show that the RRAM-based MLP has less susceptibility to SEEs compared with the SRAM architecture. Improvements to the SRAM-based MLP reliability may be achieved by lowering bit-width and enlarging network size.

Original languageEnglish (US)
Title of host publication2019 IEEE International Reliability Physics Symposium, IRPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538695043
DOIs
StatePublished - May 22 2019
Event2019 IEEE International Reliability Physics Symposium, IRPS 2019 - Monterey, United States
Duration: Mar 31 2019Apr 4 2019

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2019-March
ISSN (Print)1541-7026

Conference

Conference2019 IEEE International Reliability Physics Symposium, IRPS 2019
CountryUnited States
CityMonterey
Period3/31/194/4/19

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Keywords

  • Multi-layer perceptron
  • Neuromorphic computing
  • RRAM
  • Single event upset
  • SRAM

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ye, Z., Liu, R., Barnaby, H., & Yu, S. (2019). Evaluation of Single Event Effects in SRAM and RRAM Based Neuromorphic Computing System for Inference. In 2019 IEEE International Reliability Physics Symposium, IRPS 2019 [8720490] (IEEE International Reliability Physics Symposium Proceedings; Vol. 2019-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRPS.2019.8720490