research-article

Approximate Logic Synthesis by Genetic Algorithm with an Error Rate Guarantee

Authors:

Yung-Chih Chen,

Chun-Yao WangAuthors Info & Claims

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

Pages 146 - 151

https://doi.org/10.1145/3566097.3567890

Published: 31 January 2023 Publication History

Abstract

Approximate computing is an emerging design technique for error-tolerant applications, which may improve circuit area, delay, or power consumption by trading off a circuit's correctness. In this paper, we propose a novel approximate logic synthesis approach based on genetic algorithm targeting at depth minimization with an error rate guarantee. We conduct experiments on a set of IWLS 2005 and MCNC benchmarks. The experimental results demonstrate that the depth can be reduced by up to 50%, and 22% on average under a 5% error rate constraint. As compared with the state-of-the-art method, our approach can achieve an average of 159% more depth reduction under the same 5% error rate constraint.

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Cited By

Honda YMasuda YIshihara T(2023)Feedback-Tuned Fuzzing for Accelerating Quality Verification of Approximate Computing Design2023 IEEE 29th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS59296.2023.10224891(1-3)Online publication date: 3-Jul-2023
https://doi.org/10.1109/IOLTS59296.2023.10224891

Index Terms

Approximate Logic Synthesis by Genetic Algorithm with an Error Rate Guarantee
1. Hardware
  1. Electronic design automation

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

January 2023

807 pages

ISBN:9781450397834

DOI:10.1145/3566097

General Chair:
Atsushi Takahashi
Tokyo Institute of Technology

Copyright © 2023 ACM.

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Published: 31 January 2023

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ASPDAC '23: 28th Asia and South Pacific Design Automation Conference

January 16 - 19, 2023

Tokyo, Japan

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ASPDAC '23 Paper Acceptance Rate 102 of 328 submissions, 31%;

Overall Acceptance Rate 466 of 1,454 submissions, 32%

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Cited By

Honda YMasuda YIshihara T(2023)Feedback-Tuned Fuzzing for Accelerating Quality Verification of Approximate Computing Design2023 IEEE 29th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS59296.2023.10224891(1-3)Online publication date: 3-Jul-2023
https://doi.org/10.1109/IOLTS59296.2023.10224891

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