Impact of Multilevel Retention Characteristics on RRAM based DNN Inference Engine

Wonbo Shim, Jian Meng, Xiaochen Peng, Jae Sun Seo, Shimeng Yu

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

Abstract

In this work, the retention characteristics of multilevel HfO2 resistive random access memory (RRAM) based synaptic array was statistically measured from a 90 nm test chip and modeled at different temperatures. We found that not only the average conductance (especially at the intermediate states) drifts but also the variance of conductance exacerbates at elevated temperatures. To investigate the impact of the synaptic weight drift on deep neural network, the experimental data are modeled into the ResNet-18 simulation with 1-4 weight bit precisions. The result shows that the inference accuracy drops significantly at 55°C or above, which implies further engineering on RRAM retention or circuit/algorithmic techniques are yet to be applied.

Original languageEnglish (US)
Title of host publication2021 IEEE International Reliability Physics Symposium, IRPS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728168937
DOIs
StatePublished - Mar 2021
Event2021 IEEE International Reliability Physics Symposium, IRPS 2021 - Virtual, Monterey, United States
Duration: Mar 21 2021Mar 24 2021

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2021-March
ISSN (Print)1541-7026

Conference

Conference2021 IEEE International Reliability Physics Symposium, IRPS 2021
CountryUnited States
CityVirtual, Monterey
Period3/21/213/24/21

Keywords

  • data retention
  • multilevel RRAM
  • neural network

ASJC Scopus subject areas

  • Engineering(all)

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