XBM: A Crossbar Column-wise Binary Mask Learning Method for Efficient Multiple Task Adaption

Fan Zhang, Li Yang, Jian Meng, Yu Kevin Cao, Jae Sun Seo, Deliang Fan

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

2 Scopus citations

Abstract

Recently, utilizing ReRAM crossbar array to accelerate DNN inference on single task has been widely studied. However, using the crossbar array for multiple task adaption has not been well explored. In this paper, for the first time, we propose XBM, a novel crossbar column-wise binary mask learning method for multiple task adaption in ReRAM crossbar DNN accelerator. XBM leverages the mask-based learning algorithm's benefit to avoid catastrophic forgetting to learn a task-specific mask for each new task. With our hardware-aware design innovation, the required masking operation to adapt for a new task could be easily implemented in existing crossbar based convolution engine with minimal hardware/ memory overhead and, more importantly, no need of power hungry cell re-programming, unlike prior works. The extensive experimental results show that compared with state-of-the-art multiple task adaption methods, XBM keeps the similar accuracy on new tasks while only requires 1.4% mask memory size compared with popular piggyback. Moreover, the elimination of cell re-programming or tuning saves up to 40% energy during new task adaption.

Original languageEnglish (US)
Title of host publicationASP-DAC 2022 - 27th Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages610-615
Number of pages6
ISBN (Electronic)9781665421355
DOIs
StatePublished - 2022
Event27th Asia and South Pacific Design Automation Conference, ASP-DAC 2022 - Virtual, Online, Taiwan, Province of China
Duration: Jan 17 2022Jan 20 2022

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2022-January

Conference

Conference27th Asia and South Pacific Design Automation Conference, ASP-DAC 2022
Country/TerritoryTaiwan, Province of China
CityVirtual, Online
Period1/17/221/20/22

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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