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Approximate Computing and the Efficient Machine Learning Expedition

Authors:

Anand Raghunathan,

Mehdi B. Tahoori,

Swagath Venkataramani,

Georgios ZervakisAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 80, Pages 1 - 9

https://doi.org/10.1145/3508352.3561105

Published: 22 December 2022 Publication History

Abstract

Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.

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Cited By

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https://doi.org/10.3390/electronics13061164
Kokkinis AZervakis GSiozios KTahoori MHenkel J(2023)Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137161(1-2)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137161
Yang XLi SZheng QChen YTakahashi A(2023)Improving the Robustness and Efficiency of PIM-Based Architecture by SW/HW Co-DesignProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568358(618-623)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568358
Show More Cited By

Index Terms

Approximate Computing and the Efficient Machine Learning Expedition
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Integrated circuits
    1. Logic circuits
  2. Very large scale integration design
    1. Analog and mixed-signal circuits

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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

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Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 22 December 2022

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

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Cited By

Abbas AMaksymov I(2024)Reservoir Computing Using Measurement-Controlled Quantum DynamicsElectronics10.3390/electronics1306116413:6(1164)Online publication date: 21-Mar-2024
https://doi.org/10.3390/electronics13061164
Kokkinis AZervakis GSiozios KTahoori MHenkel J(2023)Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137161(1-2)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137161
Yang XLi SZheng QChen YTakahashi A(2023)Improving the Robustness and Efficiency of PIM-Based Architecture by SW/HW Co-DesignProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568358(618-623)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568358
Yokoyama AFerro Mde Paula FVieira VSchulze B(2023)Investigating hardware and software aspects in the energy consumption of machine learning: A green AI‐centric analysisConcurrency and Computation: Practice and Experience10.1002/cpe.782535:24Online publication date: Jun-2023
https://doi.org/10.1002/cpe.7825

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