research-article

Energy-efficient and Reliable Inference in Nonvolatile Memory under Extreme Operating Conditions

Authors:
Salonik Resch

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
S. Karen Khatamifard

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Zamshed I. Chowdhury

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Masoud Zabihi

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Zhengyang Zhao

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Husrev Cilasun

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Jian-Ping Wang

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Sachin S. Sapatnekar

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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,
Ulya R. Karpuzcu

University of Minnesota, Twin Cities, Minnesota, USA

University of Minnesota, Twin Cities, Minnesota, USA
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Authors Info & Claims

ACM Transactions on Embedded Computing Systems Volume 21 Issue 5Article No.: 57pp 1–36https://doi.org/10.1145/3520130

Published:09 December 2022Publication History

ACM Transactions on Embedded Computing Systems

Abstract

Beyond-edge devices can operate outside the reach of the power grid and without batteries. Such devices can be deployed in large numbers in regions that are difficult to access. Using machine learning, these devices can solve complex problems and relay valuable information back to a host. Many such devices deployed in low Earth orbit can even be used as nanosatellites. Due to the harsh and unpredictable nature of the environment, these devices must be highly energy-efficient, be capable of operating intermittently over a wide temperature range, and be tolerant of radiation. Here, we propose a non-volatile processing-in-memory architecture that is extremely energy-efficient, supports minimal overhead checkpointing for intermittent computing, can operate in a wide range of temperatures, and has a natural resilience to radiation.

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Energy-efficient and Reliable Inference in Nonvolatile Memory under Extreme Operating Conditions
1. Computer systems organization
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Published in
ACM Transactions on Embedded Computing Systems Volume 21, Issue 5
September 2022
526 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/3561947
Editor:
Tulika Mitra
National University of Singapore, Singapore
Issue’s Table of Contents
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Publication History
- Published: 9 December 2022
- Online AM: 4 March 2022
- Accepted: 19 February 2022
- Revised: 29 January 2022
- Received: 15 July 2021
Published in tecs Volume 21, Issue 5

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Energy-efficient and Reliable Inference in Nonvolatile Memory under Extreme Operating Conditions

ACM Transactions on Embedded Computing Systems

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