Robust RRAM-based In-Memory Computing in Light of Model Stability

Gokul Krishnan, Jingbo Sun, Jubin Hazra, Xiaocong Du, Maximilian Liehr, Zheng Li, Karsten Beckmann, Rajiv V. Joshi, Nathaniel C. Cady, Yu Cao

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

6 Scopus citations


Resistive random-access memory (RRAM)-based in-memory computing (IMC) architectures offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM-based IMC is limited by device nonidealities, ADC precision, and algorithm properties. To address this, in this work, first, we perform statistical characterization of RRAM device variation and temporal degradation from 300mm wafers of a fully integrated CMOS/RRAM 1T1R test chip at 65nm. Through this, we build a realistic foundation to assess the robustness. Second, we develop a cross-layer simulation tool that incorporates device, circuit, architecture, and algorithm properties under a single roof for system evaluation. Finally, we propose a novel loss landscape-based DNN model selection for stability, which effectively tolerates device variations and achieves a post-mapping accuracy higher than that with 50% lower RRAM variations. We demonstrate the proposed method for different DNNs on both CIFAR-10 and CIFAR-100 datasets.

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
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
ISSN (Print)1541-7026


Conference2021 IEEE International Reliability Physics Symposium, IRPS 2021
Country/TerritoryUnited States
CityVirtual, Monterey


  • Deep Neural Network
  • In-Memory Computing
  • Model Stability
  • RRAM
  • Robustness

ASJC Scopus subject areas

  • Engineering(all)


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