Efficient Multi-task Adaption for Crossbar-based In-Memory Computing

Fan Zhang, Li Yang, Deliang Fan

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

Abstract

ReRAM crossbar Non-volatile memory (NVM) based In-Memory Computing (IMC) has been widely investigated as a highly parallel, fast, and energy-efficient computing platform for Deep Neural Networks (DNNs), especially for one specific task inference. However, due to the intrinsic high energy consumption of weight re-programming and the relatively low endurance issue, adapting the ReRAM crossbar-based IMC hardware for continual learning or multi-task learning has not been well explored. In this paper, we discuss a crossbar-aware learning method with a 2-tier masking technique that could enable efficient and fast new task adaption for a deployed DNN model with minor hardware overhead.

Original languageEnglish (US)
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages328-333
Number of pages6
ISBN (Electronic)9781665459068
DOIs
StatePublished - 2022
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 2 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/2/22

Keywords

  • Deep Neural Networks
  • In-Memory Computing
  • Multi-task Continual Learning

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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