research-article

SyFAxO-GeN: Synthesizing FPGA-Based Approximate Operators with Generative Networks

Authors:

Siva Satyendra Sahoo,

Akash KumarAuthors Info & Claims

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

Pages 402 - 409

https://doi.org/10.1145/3566097.3567891

Published: 31 January 2023 Publication History

Abstract

With rising trends of moving AI inference to the edge, due to communication and privacy challenges, there has been a growing focus on designing low-cost Edge-AI. Given the diversity of application areas at the edge, FPGA-based systems are increasingly used for high-performance inference. Similarly, approximate computing has emerged as a viable approach to achieve disproportionate resource gains by utilizing the applications' inherent robustness. However, most related research has focused on selecting the appropriate approximate operators for an application from a set of ASIC-based designs. This approach fails to leverage the FPGA's architectural benefits and limits the scope of approximation to already existing generic designs. To this end, we propose an AI-based approach to synthesizing novel approximate operators for FPGA's Look-up-table-based structure. Specifically, we use state-of-the-art generative networks to search for constraint-aware arithmetic operator designs optimized for FPGA-based implementation. With the proposed GANs, we report up to 49% faster training, with negligible accuracy degradation, than related generative networks. Similarly, we report improved hypervolume and increased pareto-front design points compared to state-of-the-art approaches to synthesizing approximate multipliers.

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Cited By

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: 30-Apr-2024
https://dl.acm.org/doi/10.1145/3648694
Sahoo SUllah SKumar A(2023)AxOTreeS: A Tree Search Approach to Synthesizing FPGA-based Approximate OperatorsACM Transactions on Embedded Computing Systems10.1145/360909622:5s(1-26)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3609096
Liu AWu YWang QMao ZLiu LHan JJiang H(2023)Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic CircuitsDesign and Applications of Emerging Computer Systems10.1007/978-3-031-42478-6_14(353-381)Online publication date: 17-Aug-2023
https://doi.org/10.1007/978-3-031-42478-6_14

Index Terms

SyFAxO-GeN: Synthesizing FPGA-Based Approximate Operators with Generative Networks
1. Hardware
  1. Integrated circuits
    1. Reconfigurable logic and FPGAs
      1. Hardware accelerators

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

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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

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: 30-Apr-2024
https://dl.acm.org/doi/10.1145/3648694
Sahoo SUllah SKumar A(2023)AxOTreeS: A Tree Search Approach to Synthesizing FPGA-based Approximate OperatorsACM Transactions on Embedded Computing Systems10.1145/360909622:5s(1-26)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3609096
Liu AWu YWang QMao ZLiu LHan JJiang H(2023)Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic CircuitsDesign and Applications of Emerging Computer Systems10.1007/978-3-031-42478-6_14(353-381)Online publication date: 17-Aug-2023
https://doi.org/10.1007/978-3-031-42478-6_14

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