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Resilient circuits: enabling energy-efficient performance and reliability

Authors:

Tanay KarnikAuthors Info & Claims

ICCAD '09: Proceedings of the 2009 International Conference on Computer-Aided Design

Pages 71 - 73

https://doi.org/10.1145/1687399.1687414

Published: 02 November 2009 Publication History

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Abstract

Voltage and frequency margins necessary to ensure correct processor operation under dynamic voltage, temperature, and aging variations result in performance and power overheads. Resilient circuit techniques, including embedded error-detection sequentials and tunable replica circuits, allow these margins to be reduced or eliminated, resulting in reliable, energy-efficient operation.

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Cited By

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Lin YCavallaro J(2018)Energy-efficient Convolutional Neural Networks via Statistical Error Compensated Near Threshold Computing2018 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2018.8351679(1-5)Online publication date: May-2018
https://doi.org/10.1109/ISCAS.2018.8351679
Lin ZChen JChen L(2018)A 65 fps Full-HD Hardware Implementation of HOG, HOF, MBHx, and MBHy for Real-Time Action Recognition2018 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2018.8350912(1-5)Online publication date: 2018
https://doi.org/10.1109/ISCAS.2018.8350912
Lin YZhang SShanbhag N(2018)A Rank Decomposed Statistical Error Compensation Technique for Robust Convolutional Neural Networks in the Near Threshold Voltage RegimeJournal of Signal Processing Systems10.1007/s11265-018-1332-490:10(1439-1451)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11265-018-1332-4
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Index Terms

Resilient circuits: enabling energy-efficient performance and reliability
1. Hardware
  1. Emerging technologies
  2. Very large scale integration design

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Information

Published In

ICCAD '09: Proceedings of the 2009 International Conference on Computer-Aided Design

November 2009

803 pages

ISBN:9781605588001

DOI:10.1145/1687399

General Chair:
Jaijeet Roychowdhury
Univ. of California, Berkeley, Berkeley, California

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2009

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ICCAD '09

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SIGDA

ICCAD '09: The International Conference on Computer-Aided Design

November 2 - 5, 2009

California, San Jose

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

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Lin YCavallaro J(2018)Energy-efficient Convolutional Neural Networks via Statistical Error Compensated Near Threshold Computing2018 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2018.8351679(1-5)Online publication date: May-2018
https://doi.org/10.1109/ISCAS.2018.8351679
Lin ZChen JChen L(2018)A 65 fps Full-HD Hardware Implementation of HOG, HOF, MBHx, and MBHy for Real-Time Action Recognition2018 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2018.8350912(1-5)Online publication date: 2018
https://doi.org/10.1109/ISCAS.2018.8350912
Lin YZhang SShanbhag N(2018)A Rank Decomposed Statistical Error Compensation Technique for Robust Convolutional Neural Networks in the Near Threshold Voltage RegimeJournal of Signal Processing Systems10.1007/s11265-018-1332-490:10(1439-1451)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11265-018-1332-4
Lin CWan LChen D(2017)C-MineACM Transactions on Design Automation of Electronic Systems10.1145/314453423:2(1-23)Online publication date: 29-Nov-2017
https://dl.acm.org/doi/10.1145/3144534
Zhang SShanbhag NFanucci LTeich J(2016)Probabilistic error models for machine learning kernels implemented on stochastic nanoscale fabricsProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2971919(481-486)Online publication date: 14-Mar-2016
https://dl.acm.org/doi/10.5555/2971808.2971919
Lin YZhang SShanbhag N(2016)Variation-Tolerant Architectures for Convolutional Neural Networks in the Near Threshold Voltage Regime2016 IEEE International Workshop on Signal Processing Systems (SiPS)10.1109/SiPS.2016.11(17-22)Online publication date: Oct-2016
https://doi.org/10.1109/SiPS.2016.11
Zhang SShanbhag NMarculescu DLiu F(2015)Reduced Overhead Error Compensation for Energy Efficient Machine Learning KernelsProceedings of the IEEE/ACM International Conference on Computer-Aided Design10.5555/2840819.2840822(15-21)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.5555/2840819.2840822
Zhang SShanbhag N(2015)Reduced overhead error compensation for energy efficient machine learning kernels2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)10.1109/ICCAD.2015.7372544(15-21)Online publication date: Nov-2015
https://doi.org/10.1109/ICCAD.2015.7372544
Sartori JKumar RXie YKarnik TKhellah MMehra R(2014)Software canariesProceedings of the 2014 international symposium on Low power electronics and design10.1145/2627369.2627646(159-164)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1145/2627369.2627646
Lin CWan LChen D(2014)C-MineProceedings of the 51st Annual Design Automation Conference10.1145/2593069.2593107(1-6)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2593069.2593107
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Circuit techniques for dynamic variation tolerance

Resilient microprocessor design for improving performance and energy efficiency

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