Thermal-aware optimizations of ReRAM-based neuromorphic computing systems

Majed Valad Beigi, Gokhan Memik

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

10 Scopus citations

Abstract

ReRAM-based systems are attractive implementation alternatives for neuromorphic computing because of their high speed and low design cost. In this work, we investigate the impact of temperature on the ReRAM-based neuromorphic architectures and show how varying temperatures have a negative impact on the computation accuracy. We first classify ReRAM crossbar cells based on their temperature and identify effective neural network weights that have large impacts on network outputs. Then, we propose a novel temperature-aware training and mapping scheme to prevent the effective weights from being mapped to hot cells to restore the system accuracy. Evaluation results for a two-layer neural network show that our scheme can improve the system accuracy by up to 39.2%.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357005
DOIs
StatePublished - Jun 24 2018
Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
Duration: Jun 24 2018Jun 29 2018

Publication series

NameProceedings - Design Automation Conference
VolumePart F137710
ISSN (Print)0738-100X

Other

Other55th Annual Design Automation Conference, DAC 2018
CountryUnited States
CitySan Francisco
Period6/24/186/29/18

Keywords

  • Neuromorphic Computing
  • ReRAM
  • Temperature

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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