research-article

Approximate computing and the quest for computing efficiency

Authors:

Swagath Venkataramani,

Srimat T. Chakradhar,

Anand RaghunathanAuthors Info & Claims

DAC '15: Proceedings of the 52nd Annual Design Automation Conference

Article No.: 120, Pages 1 - 6

https://doi.org/10.1145/2744769.2751163

Published: 07 June 2015 Publication History

Abstract

Diminishing benefits from technology scaling have pushed designers to look for new sources of computing efficiency. Multicores and heterogeneous accelerator-based architectures are a by-product of this quest to obtain improvements in the performance of computing platforms at similar or lower power budgets. In light of the need for new innovations to sustain these improvements, we discuss approximate computing, a field that has attracted considerable interest over the last decade. While the core principles of approximate computing---computing efficiently by producing results that are good enough or of sufficient quality---are not new and are shared by many fields from algorithm design to networks and distributed systems, recent e.orts have seen a percolation of these principles to all layers of the computing stack, including circuits, architecture, and software. Approximate computing techniques have also evolved from ad hoc and applicationspecific to more broadly applicable, supported by systematic design methodologies. Finally, the emergence of workloads such as recognition, mining, search, data analytics, inference and vision are greatly increasing the opportunities for approximate computing. We describe the vision and key principles that have guided our work in this area, and outline a holistic cross-layer framework for approximate computing.

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Cited By

Leon VHanif MArmeniakos GJiao XShafique MPekmestzi KSoudris D(2025)Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and ApplicationsACM Computing Surveys10.1145/371168357:7(1-36)Online publication date: 20-Feb-2025
https://dl.acm.org/doi/10.1145/3711683
Gong NWang JJin WDas HHaidous A(2025)AI-Enabled Efficient Memory Design for Data-Intensive ApplicationsAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_3(83-108)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-71436-8_3
Saeedi SPiri ADeveautour BO’Connor IBosio ASavino ADi Carlo S(2024)A Survey on Design Space Exploration Approaches for Approximate Computing SystemsElectronics10.3390/electronics1322444213:22(4442)Online publication date: 13-Nov-2024
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Approximate computing and the quest for computing efficiency

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cover image ACM Conferences

DAC '15: Proceedings of the 52nd Annual Design Automation Conference

June 2015

1204 pages

ISBN:9781450335201

DOI:10.1145/2744769

Copyright © 2015 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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Published: 07 June 2015

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DAC '15: The 52nd Annual Design Automation Conference 2015

June 7 - 11, 2015

California, San Francisco

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Cited By

Leon VHanif MArmeniakos GJiao XShafique MPekmestzi KSoudris D(2025)Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and ApplicationsACM Computing Surveys10.1145/371168357:7(1-36)Online publication date: 20-Feb-2025
https://dl.acm.org/doi/10.1145/3711683
Gong NWang JJin WDas HHaidous A(2025)AI-Enabled Efficient Memory Design for Data-Intensive ApplicationsAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_3(83-108)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-71436-8_3
Saeedi SPiri ADeveautour BO’Connor IBosio ASavino ADi Carlo S(2024)A Survey on Design Space Exploration Approaches for Approximate Computing SystemsElectronics10.3390/electronics1322444213:22(4442)Online publication date: 13-Nov-2024
https://doi.org/10.3390/electronics13224442
Sahoo SUllah SKumar A(2024)AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical ProgrammingACM Transactions on Reconfigurable Technology and Systems10.1145/364869417:2(1-28)Online publication date: 19-Feb-2024
https://dl.acm.org/doi/10.1145/3648694
Manikandababu CJagadeeswari MJanani RBanu M(2024)Efficient Error-Tolerant Computation: Static Segmented Multipliers with Inner Approximation2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10602240(1-7)Online publication date: 9-May-2024
https://doi.org/10.1109/ACCAI61061.2024.10602240
Bosio AGerminiani SPravadelli GTraiola M(2024)Syntactic and Semantic Analysis of Temporal Assertions to Support the Approximation of RTL DesignsJournal of Electronic Testing10.1007/s10836-024-06115-940:2(199-214)Online publication date: 23-Apr-2024
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Guturu SUppugunduru ASharma AAhmed S(2024)A Power-Efficient Gaussian Filter Architecture Using Adder Compressors for Edge Detection ApplicationCircuits, Systems, and Signal Processing10.1007/s00034-024-02894-wOnline publication date: 28-Oct-2024
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Hanif MHafiz RShafique M(2023)DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate AddersInformation10.3390/info1410057014:10(570)Online publication date: 17-Oct-2023
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Balasubramanian PMaskell D(2023)FAC: A Fault-Tolerant Design Approach Based on Approximate ComputingElectronics10.3390/electronics1218381912:18(3819)Online publication date: 9-Sep-2023
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Javadi MFaryabi MMahdiani H(2023)A Comprehensive Model for Efficient Design Space Exploration of Imprecise Computational BlocksACM Transactions on Embedded Computing Systems10.1145/362555522:6(1-20)Online publication date: 9-Nov-2023
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