research-article

Analysis and characterization of inherent application resilience for approximate computing

Authors:

Vinay K. Chippa,

Srimat T. Chakradhar,

Anand RaghunathanAuthors Info & Claims

DAC '13: Proceedings of the 50th Annual Design Automation Conference

Article No.: 113, Pages 1 - 9

https://doi.org/10.1145/2463209.2488873

Published: 29 May 2013 Publication History

Abstract

Approximate computing is an emerging design paradigm that enables highly efficient hardware and software implementations by exploiting the inherent resilience of applications to in-exactness in their computations. Previous work in this area has demonstrated the potential for significant energy and performance improvements, but largely consists of ad hoc techniques that have been applied to a small number of applications. Taking approximate computing closer to mainstream adoption requires (i) a deeper understanding of inherent application resilience across a broader range of applications (ii) tools that can quantitatively establish the inherent resilience of an application, and (iii) methods to quickly assess the potential of various approximate computing techniques for a given application. We make two key contributions in this direction. Our primary contribution is the analysis and characterization of inherent application resilience present in a suite of 12 widely used applications from the domains of recognition, data mining, and search. Based on this analysis, we present several new insights into the nature of resilience and its relationship to various key application characteristics. To facilitate our analysis, we propose a systematic framework for Application Resilience Characterization (ARC) that (a) partitions an application into resilient and sensitive parts and (b) characterizes the resilient parts using approximation models that abstract a wide range of approximate computing techniques. We believe that the key insights that we present can help shape further research in the area of approximate computing, while automatic resilience characterization frameworks such as ARC can greatly aid designers in the adoption approximate computing.

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Cited By

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https://dl.acm.org/doi/10.1145/3686625.3686631
Li ZWang QZou ZShen QXie NCai HZhang HLiu B(2024)Layer-Sensitive Neural Processing Architecture for Error-Tolerant ApplicationsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.336964832:5(797-809)Online publication date: May-2024
https://doi.org/10.1109/TVLSI.2024.3369648
Jha CHassan MDrechsler R(2024) cecApprox: Enabling Automated Combinational Equivalence Checking for Approximate Circuits IEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.338825671:7(3282-3293)Online publication date: Jul-2024
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Index Terms

Analysis and characterization of inherent application resilience for approximate computing
1. Hardware
  1. Integrated circuits

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cover image ACM Conferences

DAC '13: Proceedings of the 50th Annual Design Automation Conference

May 2013

1285 pages

ISBN:9781450320719

DOI:10.1145/2463209

Copyright © 2013 ACM.

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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New York, NY, United States

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Published: 29 May 2013

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DAC '13: The 50th Annual Design Automation Conference 2013

May 29 - June 7, 2013

Texas, Austin

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Nagatomo TSato T(2024)Cost-effective Alternatives for Squarer in Pan-Tompkins AlgorithmProceedings of the 2024 6th International Electronics Communication Conference10.1145/3686625.3686631(31-36)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3686625.3686631
Li ZWang QZou ZShen QXie NCai HZhang HLiu B(2024)Layer-Sensitive Neural Processing Architecture for Error-Tolerant ApplicationsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.336964832:5(797-809)Online publication date: May-2024
https://doi.org/10.1109/TVLSI.2024.3369648
Jha CHassan MDrechsler R(2024) cecApprox: Enabling Automated Combinational Equivalence Checking for Approximate Circuits IEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.338825671:7(3282-3293)Online publication date: Jul-2024
https://doi.org/10.1109/TCSI.2024.3388256
Niu ZZhang TJiang HCockburn BLiu LHan J(2024)Hardware-Efficient Logarithmic Floating-Point Multipliers for Error-Tolerant ApplicationsIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2023.332632971:1(209-222)Online publication date: Jan-2024
https://doi.org/10.1109/TCSI.2023.3326329
Tegazzini LDi Meo GDe Caro DStrollo A(2024)Design of a Hardware-Efficient Floating-Point Multiplier with Dynamic Segmentation2024 19th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)10.1109/PRIME61930.2024.10559705(1-4)Online publication date: 9-Jun-2024
https://doi.org/10.1109/PRIME61930.2024.10559705
Freytag GKünas CRech PNavaux P(2024)Interleaved Execution of Approximated CUDA Kernels in Iterative Applications2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)10.1109/PDP62718.2024.00017(60-67)Online publication date: 20-Mar-2024
https://doi.org/10.1109/PDP62718.2024.00017
Du LNi LLiu XPeng GLi KMao WYu H(2024)A Low-Power DNN Accelerator With Mean-Error-Minimized Approximate Signed MultiplierIEEE Open Journal of Circuits and Systems10.1109/OJCAS.2023.32792515(57-68)Online publication date: 2024
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Napoli EStrollo AZacharelos EDi Meo G(2024)Comprehensive Analysis of Input Order Invariant Approximate 4-2 Compressors for Binary Multipliers2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558503(1-5)Online publication date: 19-May-2024
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M DReddy DK HN RS Y(2024)Design of Efficient Approximate Unsigned Multiplier using VariousApproximate Compressor Configurations2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)10.1109/ICSSEECC61126.2024.10649502(415-420)Online publication date: 28-Jun-2024
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Traiola MPappalardo SPiri ARuospo ADeveautour BSanchez EBosio ASaeedi SCarpegna AGöğebakan AMagliano ESavino A(2024)Approximate Fault-Tolerant Neural Network Systems2024 IEEE European Test Symposium (ETS)10.1109/ETS61313.2024.10567290(1-10)Online publication date: 20-May-2024
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