research-article

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

Authors:

S. Karen Khatamifard,

Zamshed I. Chowdhury,

Zhengyang Zhao,

Husrev Cilasun,

Jian-Ping Wang,

Sachin S. Sapatnekar,

Ulya R. KarpuzcuAuthors Info & Claims

ACM Transactions on Embedded Computing Systems, Volume 21, Issue 5

Article No.: 57, Pages 1 - 36

https://doi.org/10.1145/3520130

Published: 09 December 2022 Publication History

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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Index Terms

Energy-efficient and Reliable Inference in Nonvolatile Memory under Extreme Operating Conditions
1. Computer systems organization
  1. Architectures

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cover image ACM Transactions on Embedded Computing Systems

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

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Published: 09 December 2022

Online AM: 04 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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Barjami RMiele AMottola LShu YLiu JTan RHe YChen J(2024)Intermittent Inference: Trading a 1% Accuracy Loss for a 1.9x Throughput SpeedupProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699364(647-660)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699364
Akhunov KYldrm K(2024)CRAM-Based Acceleration for Intermittent Computing of Parallelizable TasksIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.329342612:1(48-59)Online publication date: Jan-2024
https://doi.org/10.1109/TETC.2023.3293426
Caronti LAkhunov KNardello MYıldırım KBrunelli D(2023)Fine-grained Hardware Acceleration for Efficient Batteryless Intermittent Inference on the EdgeACM Transactions on Embedded Computing Systems10.1145/360847522:5(1-19)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3608475
Shen ZJin JTan CTagami AWang SLi QZheng QYuan J(2023)A Survey of Next-generation Computing Technologies in Space-air-ground Integrated NetworksACM Computing Surveys10.1145/360601856:1(1-40)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3606018
Lv MXu E(2022)Deep Learning on Energy Harvesting IoT Devices: Survey and Future ChallengesIEEE Access10.1109/ACCESS.2022.322509210(124999-125014)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3225092

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