On the Reliability of RRAM-Based Neural Networks

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Emerging device technologies such as Resistive RAMs (RRAMs) are under investigation by many researchers and semiconductor companies; not only to realize e.g., embedded non-volatile memories, but also to enable energy-efficient computing making use of new data processing paradigms such as computation-in-memory. However, such devices suffer from various non-idealities and reliability failure mechanisms (e.g., variability, endurance, and retention); these negatively impact the memory robustness and the computation accuracy. This paper discusses the non-idealities and reliability failure mechanisms for RRAM devices, provides an overview on the most popular ones. In addition, it reports detailed anlysis of some of these based on data measurements. Finally, it presents two different mitigation schemes for RRAM based accelerators; one is based on RRAM non-ideality aware quantization and conductance control for neural network accuracy enhancement while the second is based on reliability-aware biased training technique.
Original languageEnglish
Title of host publicationProceedings of the 2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC)
PublisherIEEE
Number of pages8
ISBN (Electronic)979-8-3503-2599-7
ISBN (Print)979-8-3503-2600-0
DOIs
Publication statusPublished - 2023
Event2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC) - Dubai, United Arab Emirates
Duration: 16 Oct 202318 Oct 2023
Conference number: 31st

Conference

Conference2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC)
Country/TerritoryUnited Arab Emirates
City Dubai
Period16/10/2318/10/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • RRAM
  • reliability
  • neural network
  • in-memory computing

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