MOUSE: Inference in non-volatile memory for energy harvesting applications

Salonik Resch, S. Karen Khatamifard, Zamshed I. Chowdhury, Masoud Zabihi, Zhengyang Zhao, Husrev Cilasun, Jianping Wang, Sachin S. Sapatnekar, Ulya Karpuzcu

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

Abstract

There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities of low power devices, a number of new applications become possible. In some applications, such devices will not even have a battery, and must rely solely on energy harvesting techniques. This puts extreme constraints on the hardware, which must be energy efficient and capable of tolerating interruptions due to power outages. Here, we propose an in-memory machine learning accelerator utilizing non-volatile spintronic memory. The combination of processing-in-memory and non-volatility provides a key advantage in that progress is effectively saved after every operation. This enables instant shut down and restart capabilities with minimal overhead. Additionally, the operations are highly energy efficient leading to low power consumption.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020
PublisherIEEE Computer Society
Pages400-414
Number of pages15
ISBN (Electronic)9781728173832
DOIs
StatePublished - Oct 2020
Event53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020 - Virtual, Athens, Greece
Duration: Oct 17 2020Oct 21 2020

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
Volume2020-October
ISSN (Print)1072-4451

Conference

Conference53rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2020
CountryGreece
CityVirtual, Athens
Period10/17/2010/21/20

Bibliographical note

Funding Information:
This work was supported in part by NSF under Grant SPX-1725420

Keywords

  • Intermittent computing
  • Processing-in-Memory

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