research-article

ApproxIt: An Approximate Computing Framework for Iterative Methods

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Qiang XuAuthors Info & Claims

DAC '14: Proceedings of the 51st Annual Design Automation Conference

Pages 1 - 6

https://doi.org/10.1145/2593069.2593092

Published: 01 June 2014 Publication History

Abstract

Approximate computing, being able to tradeoff computation quality (e.g., accuracy) and computational effort (e.g., energy) for error-tolerant applications such as media processing and the emerging Recognition, Mining, and Synthesis (RMS) applications, has gained significant traction in recent years. Many of these applications employ iterative methods for solution-finding, wherein a sequence of improving approximate solutions are generated before reaching the final converged solution. In this work, we propose ApproxIt, a novel approximate computing framework for iterative methods with quality guarantees. To be specific, we present a lightweight quality estimator that is able to capture the solution quality of each iteration and use it to guide the selection of approximate computing mode in the next iteration. With the proposed dynamic effort scaling technique, ApproxIt is able to dramatically improve application energy efficiency under quality guarantees, as demonstrated in our experimental results.

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Cited By

Azhar MManivannan MStenström P(2023)Approx-RM: Reducing Energy on Heterogeneous Multicore Processors under Accuracy and Timing ConstraintsACM Transactions on Architecture and Code Optimization10.1145/360521420:3(1-25)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3605214
Raza MJaved SKazmi MAziz AUl Haque MQazi S(2023)Approximate Computing: Hardware and Software Techniques, Tools and Their ApplicationsJournal of Circuits, Systems and Computers10.1142/S021812662430001033:04Online publication date: 20-Sep-2023
https://doi.org/10.1142/S0218126624300010
Bonnot JMercat ANogues EMénard D(2022)Approximate Computing at the Algorithmic LevelApproximate Computing Techniques10.1007/978-3-030-94705-7_5(109-142)Online publication date: 3-Jan-2022
https://doi.org/10.1007/978-3-030-94705-7_5
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cover image ACM Other conferences

DAC '14: Proceedings of the 51st Annual Design Automation Conference

June 2014

1249 pages

ISBN:9781450327305

DOI:10.1145/2593069

Copyright © 2014 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
SIGBED: ACM Special Interest Group on Embedded Systems
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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Association for Computing Machinery

New York, NY, United States

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Published: 01 June 2014

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DAC '14

DAC '14: The 51st Annual Design Automation Conference 2014

June 1 - 5, 2014

CA, San Francisco, USA

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

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Cited By

Azhar MManivannan MStenström P(2023)Approx-RM: Reducing Energy on Heterogeneous Multicore Processors under Accuracy and Timing ConstraintsACM Transactions on Architecture and Code Optimization10.1145/360521420:3(1-25)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3605214
Raza MJaved SKazmi MAziz AUl Haque MQazi S(2023)Approximate Computing: Hardware and Software Techniques, Tools and Their ApplicationsJournal of Circuits, Systems and Computers10.1142/S021812662430001033:04Online publication date: 20-Sep-2023
https://doi.org/10.1142/S0218126624300010
Bonnot JMercat ANogues EMénard D(2022)Approximate Computing at the Algorithmic LevelApproximate Computing Techniques10.1007/978-3-030-94705-7_5(109-142)Online publication date: 3-Jan-2022
https://doi.org/10.1007/978-3-030-94705-7_5
赵磊(2021)Comparison of Several Common Approximate AddersOpen Journal of Circuits and Systems10.12677/OJCS.2021.10300310:03(15-23)Online publication date: 2021
https://doi.org/10.12677/OJCS.2021.103003
Bu TYan KTan J(2021)Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUsACM Transactions on Design Automation of Electronic Systems10.1145/348661227:2(1-19)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486612
Yu HHa YVeeravalli BChen FEl-Sayed H(2021)DVFS-Based Quality Maximization for Adaptive Applications With Diminishing ReturnIEEE Transactions on Computers10.1109/TC.2020.299724270:5(803-816)Online publication date: 1-May-2021
https://doi.org/10.1109/TC.2020.2997242
Hou XWang XPalesi MSingh AJiang YYang MHuang LChen J(2021)On Pareto-frontier Approximate Computing for Many-core Systems2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)10.1109/ICITES53477.2021.9637071(1-8)Online publication date: 31-Oct-2021
https://doi.org/10.1109/ICITES53477.2021.9637071
Dickerson JGalanis ITasoulas ZKinley LAnagnostopoulos I(2020)Adaptive Approximate Computing on Hardware Accelerators Targeting Internet-of-Things2020 IEEE 6th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT48130.2020.9221165(1-6)Online publication date: Jun-2020
https://doi.org/10.1109/WF-IoT48130.2020.9221165
Zhang QXu Q(2020)ApproxIt: A Quality Management Framework of Approximate Computing for Iterative MethodsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277523639:5(991-1002)Online publication date: May-2020
https://doi.org/10.1109/TCAD.2017.2775236
Traiola MVirazel AGirard PBarbareschi MBosio A(2020)A Survey of Testing Techniques for Approximate Integrated CircuitsProceedings of the IEEE10.1109/JPROC.2020.2999613108:12(2178-2194)Online publication date: Dec-2020
https://doi.org/10.1109/JPROC.2020.2999613
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