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

Di Meo GGiuseppe Maria Strollo ADe Caro DTegazzini LNapoli E(2025)Low-Power High Precision Floating-Point Divider With Bidimensional Linear ApproximationIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.344783072:2(882-895)Online publication date: Feb-2025
https://doi.org/10.1109/TCSI.2024.3447830
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
Vakili SVaziri MZarei ALanglois J(2024)DyRecMul: Fast and Low-Cost Approximate Multiplier for FPGAs using Dynamic ReconfigurationACM Transactions on Reconfigurable Technology and Systems10.1145/366348018:1(1-22)Online publication date: 17-Dec-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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EDAC: Electronic Design Automation Consortium
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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2013

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DAC '13

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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

Di Meo GGiuseppe Maria Strollo ADe Caro DTegazzini LNapoli E(2025)Low-Power High Precision Floating-Point Divider With Bidimensional Linear ApproximationIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.344783072:2(882-895)Online publication date: Feb-2025
https://doi.org/10.1109/TCSI.2024.3447830
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
Vakili SVaziri MZarei ALanglois J(2024)DyRecMul: Fast and Low-Cost Approximate Multiplier for FPGAs using Dynamic ReconfigurationACM Transactions on Reconfigurable Technology and Systems10.1145/366348018:1(1-22)Online publication date: 17-Dec-2024
https://dl.acm.org/doi/10.1145/3663480
Thakur GJain S(2024)Design and Analysis of Optimized Approximate Multiplier Using Novel Higher-Order CompressorJournal of Circuits, Systems and Computers10.1142/S0218126625500665Online publication date: 21-Oct-2024
https://doi.org/10.1142/S0218126625500665
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
Kaushik NSrinivasu B(2024)High-Speed and Area-Efficient Serial IMPLY-Based Approximate Subtractor and Comparator for Image Processing and Neural NetworksIEEE Transactions on Nanotechnology10.1109/TNANO.2024.348722323(748-757)Online publication date: 2024
https://doi.org/10.1109/TNANO.2024.3487223
Di Meo GSaggese GStrollo ADe Caro D(2024)Approximate MAC Unit Using Static SegmentationIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.331530112:4(968-979)Online publication date: Oct-2024
https://doi.org/10.1109/TETC.2023.3315301
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
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