research-article

SEALS: sensitivity-driven efficient approximate logic synthesis

Authors:

Weikang QianAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 439 - 444

https://doi.org/10.1145/3489517.3530464

Published: 23 August 2022 Publication History

Abstract

Approximate computing is an emerging computing paradigm to design energy-efficient systems. Many greedy approximate logic synthesis (ALS) methods have been proposed to automatically synthesize approximate circuits. They typically need to consider all local approximate changes (LACs) in each iteration of the ALS flow to select the best one, which is time-consuming. In this paper, we propose SEALS, a Sensitivity-driven Efficient ALS method to speed up a greedy ALS flow. SEALS centers around a newly proposed concept called sensitivity, which enables a fast and accurate error estimation method and an efficient method to filter out unpromising LACs. SEALS can handle any statistical error metric. The experimental results show that it outperforms a state-of-the-art ALS method in runtime by 12X to 15X without reducing circuit quality.

References

[1]

Q. Xu, T. Mytkowicz, and N. S. Kim. Approximate computing: A survey. IEEE Des. Test, 33(1):8--22, 2015.

[2]

S. Mittal. A survey of techniques for approximate computing. ACM Comput. Surv., 48(4):62:1--62:33, 2016.

Digital Library

[3]

I. Scarabottolo, G. Ansaloni, et al. Approximate logic synthesis: A survey. Proc. IEEE, 108(12):2195--2213, 2020.

[4]

D. Shin and S. K. Gupta. A new circuit simplification method for error tolerant applications. In DATE, pages 1--6, 2011.

[5]

S. Venkataramani, K. Roy, and A. Raghunathan. Substitute-and-simplify: A unified design paradigm for approximate and quality configurable circuits. In DATE, pages 1367--1372, 2013.

Digital Library

[6]

J. Miao, A. Gerstlauer, and M. Orshansky. Multi-level approximate logic synthesis under general error constraints. In ICCAD, pages 504--510, 2014.

Digital Library

[7]

Z. Vasicek and L. Sekanina. Evolutionary approach to approximate digital circuits design. IEEE Trans. on Evol. Comput., 19(3):432--444, 2015.

Digital Library

[8]

Y. Wu and W. Qian. An efficient method for multi-level approximate logic synthesis under error rate constraint. In DAC, pages 128:1--128:6, 2016.

Digital Library

[9]

A. Chandrasekharan, T. Villa, et al. Approximation-aware rewriting of AIGs for error tolerant applications. In ICCAD, pages 83:1--83:8, 2016.

Digital Library

[10]

G. Liu and Z. Zhang. Statistically certified approximate logic synthesis. In ICCAD, pages 344--351, 2017.

Digital Library

[11]

S. Hashemi, H. Tann, and S. Reda. BLASYS: Approximate logic synthesis using Boolean matrix factorization. In DAC, pages 55:1--55:6, 2018.

Digital Library

[12]

C. Meng, W. Qian, and A. Mishchenko. ALSRAC: Approximate logic synthesis by resubstitution with approximate care set. In DAC, pages 187:1--187:6, 2020.

[13]

J. Echavarria, S. Wildermann, and J. Teich. Approximate logic synthesis of very large Boolean networks. In DATE, pages 1552--1557, 2021.

[14]

S. Su, Y. Wu, and W. Qian. Efficient batch statistical error estimation for iterative multi-level approximate logic synthesis. In DAC, pages 54:1--54:6, 2018.

[15]

J. Echavarria, S. Wildermann, et al. Probabilistic error propagation through approximated Boolean networks. In DAC, pages 1--6, 2020.

[16]

I. Scarabottolo, G. Ansaloni, et al. Partition and propagate: an error derivation algorithm for the design of approximate circuits. In DAC, pages 1--6, 2019.

Digital Library

[17]

M. Hansen et al. Unveiling the ISCAS-85 benchmarks: a case study in reverse engineering. IEEE Des. Test Comput., 16(3):72--80, 1999.

Digital Library

[18]

S. Yang. Logic synthesis and optimization benchmarks. Technical report, Micro-electronics Center of North Carolina, 1991.

Cited By

Ye YChen TGao YYan HYu BShi L(2024)Timing-Driven Technology Mapping Approximation Based on Reinforcement LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.337901643:9(2755-2768)Online publication date: Sep-2024
https://doi.org/10.1109/TCAD.2024.3379016
Zhao BQiu LLao Y(2023)Data-Driven Feature Selection Framework for Approximate Circuit DesignIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.326016042:11(3519-3531)Online publication date: 21-Mar-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3260160
Wang XYan ZMeng CShi YQian W(2023)DASALS: Differentiable Architecture Search-Driven Approximate Logic Synthesis2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323820(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323820

Recommendations

Search space characterization for approximate logic synthesis
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Approximate logic synthesis aims at trading off a circuit's quality to improve a target metric. Corresponding methods explore a search space by approximating circuit components and verifying the resulting quality of the overall circuit, which is costly.

...
Efficient batch statistical error estimation for iterative multi-level approximate logic synthesis
DAC '18: Proceedings of the 55th Annual Design Automation Conference

Approximate computing is an emerging energy-efficient paradigm for error-resilient applications. Approximate logic synthesis (ALS) is an important field of it. To improve the existing ALS flows, one key issue is to derive a more accurate and efficient ...
ALSRAC: approximate logic synthesis by resubstitution with approximate care set
DAC '20: Proceedings of the 57th ACM/EDAC/IEEE Design Automation Conference

Approximate computing is an emerging design technique for error-resilient applications. It improves circuit area, power, and delay at the cost of introducing some errors. Approximate logic synthesis (ALS) is an automatic process to produce approximate ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 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]

Sponsors

SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key R&D Program of China

Conference

DAC '22

Sponsor:

SIGDA

DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

Acceptance Rates

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

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
200
Total Downloads

Downloads (Last 12 months)61
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ye YChen TGao YYan HYu BShi L(2024)Timing-Driven Technology Mapping Approximation Based on Reinforcement LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.337901643:9(2755-2768)Online publication date: Sep-2024
https://doi.org/10.1109/TCAD.2024.3379016
Zhao BQiu LLao Y(2023)Data-Driven Feature Selection Framework for Approximate Circuit DesignIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.326016042:11(3519-3531)Online publication date: 21-Mar-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3260160
Wang XYan ZMeng CShi YQian W(2023)DASALS: Differentiable Architecture Search-Driven Approximate Logic Synthesis2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323820(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323820
Wang XTao SZhu JShi YQian W(2023)AccALS: Accelerating Approximate Logic Synthesis by Selection of Multiple Local Approximate Changes2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247856(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247856

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten