research-article

Exploiting Partially-Forgetful Memories for Approximate Computing

Authors:

Majid Shoushtari,

Abbas BanaiyanMofrad,

Nikil DuttAuthors Info & Claims

IEEE Embedded Systems Letters, Volume 7, Issue 1

Pages 19 - 22

https://doi.org/10.1109/LES.2015.2393860

Published: 01 March 2015 Publication History

Abstract

While the memory subsystem is already a major contributor to energy consumption of embedded systems, the guard-banding required for masking the effects of ever increasing manufacturing variations in memories imposes even more energy overhead. In this letter, we explore how partially-forgetful memories can be used by exploiting the intrinsic tolerance of a vast class of applications to some level of error for relaxing this guard-banding in memories. We discuss the challenges to be addressed and introduce relaxed cache as an exemplar to address these challenges for partially-forgetful SRAM caches. Preliminary results show how adapting guard-bands to application characteristics can help the system save significant amount of cache leakage energy (up to 74%) while still generating acceptable quality results.

References

[1]

ITRS, [Online]. Available: http://www.itrs.net, 2013.

[2]

J. Wang and B. H. Calhoun, “Standby supply voltage minimization for reliable nanoscale SRAMs,” in Solid State Circ. Tech., 2004, InTech.

[3]

J. Liu et al., “RAIDR: Retention-aware intelligent DRAM refresh,” in ISCA, 2012.

[4]

W. Zhang and T. Li, “Characterizing and mitigating the impact of process variations on phase change ased memory systems,” in MICRO, 2009.

[5]

A. Sampson et al., “EnerJ: Approximate data types for safe and general low-power computation,” in PLDI, 2011.

[6]

L. Leem et al., “ERSA: Error resilient system architecture for probabilistic applications,” in DATE, 2010.

[7]

M. de Kruijf et al., “Relax: An architectural framework for software recovery of hardware faults,” in ISCA, 2010.

[8]

V. K. Chippa et al., “Analysis and characterization of inherent application resilience for approximate computing,” in DAC, 2013.

[9]

S. Liu et al., “Flikker: Saving DRAM refresh-power through critical data partitioning,” in ASPLOS, 2011.

[10]

M. Carbin et al., “Verifying quantitative reliability for programs that execute on unreliable hardware,” in OOPSLA, 2013.

[11]

A. Sampson et al., “Approximate storage in solid-state memories,” in MICRO, 2013.

[12]

M. Shoushtari et al., Relaxing manufacturing guard-bands in memories for energy saving, Center for Embedded Computer Systems, Univ. California, Irvine, CA USA, Tech. Rep. CECS-TR-14-04, May 2014.

[13]

A. BanaiyanMofrad et al., “FFT-cache: A flexible fault-tolerant cache architecture for ultra low voltage operation,” in CASES, 2011.

[14]

N. Binkert et al., “The gem5 simulator,” SIGARCH Comput. Arch. News, vol. 39, no. 2, 2011.

[15]

ARM “Cortex-A5 Processor Manual,”, [Online]. Available: http://www.arm.com.

[16]

ARM “Cortex-R4 Processor Manual,”, [Online]. Available: http://www.arm.com.

[17]

M. Gottscho et al., “Power/capacity scaling: Energy savings with simple fault-tolerant caches,” in DAC, 2014.

[18]

M. R. Guthaus et al., “MiBench: A free, commercially representative embedded benchmark suite,” in WWC, 2001.

[19]

C. Bienia et al., “The PARSEC benchmark suite: Characterization and architectural implications,” in PACT, 2008.

Cited By

Damsgaard HOmetov ANurmi J(2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3572772
Ma CZhou ZHan LShen ZWang YChen RShao Z(2022)Rebirth-FTL: Lifetime Optimization via Approximate Storage for NAND Flash MemoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312317741:10(3276-3289)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/TCAD.2021.3123177
Maity BDonyanavard BSurhonne ARahmani AHerkersdorf ADutt N(2021)SEAMSACM Transactions on Embedded Computing Systems10.1145/346687520:5(1-26)Online publication date: 29-Jul-2021
https://dl.acm.org/doi/10.1145/3466875
Show More Cited By

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Exploiting Partially-Forgetful Memories for Approximate Computing
1. Hardware

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cover image IEEE Embedded Systems Letters

IEEE Embedded Systems Letters Volume 7, Issue 1

March 2015

34 pages

ISSN:1943-0663

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Copyright © 2015.

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IEEE Press

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Published: 01 March 2015

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Cited By

Damsgaard HOmetov ANurmi J(2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3572772
Ma CZhou ZHan LShen ZWang YChen RShao Z(2022)Rebirth-FTL: Lifetime Optimization via Approximate Storage for NAND Flash MemoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312317741:10(3276-3289)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/TCAD.2021.3123177
Maity BDonyanavard BSurhonne ARahmani AHerkersdorf ADutt N(2021)SEAMSACM Transactions on Embedded Computing Systems10.1145/346687520:5(1-26)Online publication date: 29-Jul-2021
https://dl.acm.org/doi/10.1145/3466875
Stanley-Marbell PAlaghi ACarbin MDarulova EDolecek LGerstlauer AGillani GJevdjic DMoreau TCacciotti MDaglis AJerger NFalsafi BMisailovic SSampson AZufferey D(2020)Exploiting Errors for EfficiencyACM Computing Surveys10.1145/339489853:3(1-39)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3394898
Widmer MBonetti ABurg A(2019)FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing SystemsProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317830(1-6)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1145/3316781.3317830
Betzel FKhatamifard KSuresh HLilja DSartori JKarpuzcu U(2018)Approximate CommunicationACM Computing Surveys10.1145/314581251:1(1-32)Online publication date: 10-Jan-2018
https://dl.acm.org/doi/10.1145/3145812
Gottscho MAlam ISchoeny CDolecek LGupta P(2017)Low-Cost Memory Fault Tolerance for IoT DevicesACM Transactions on Embedded Computing Systems10.1145/312653416:5s(1-25)Online publication date: 27-Sep-2017
https://dl.acm.org/doi/10.1145/3126534
Mittal S(2016)A Survey of Techniques for Approximate ComputingACM Computing Surveys10.1145/289335648:4(1-33)Online publication date: 18-Mar-2016
https://dl.acm.org/doi/10.1145/2893356

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