Publication Details

ApproxFPGAs: Embracing ASIC-based Approximate Arithmetic Components for FPGA-Based Systems

PRABAKARAN, B.; MRÁZEK, V.; VAŠÍČEK, Z.; SEKANINA, L.; SHAFIQUE, M. ApproxFPGAs: Embracing ASIC-based Approximate Arithmetic Components for FPGA-Based Systems. In 2020 57th ACM/IEEE Design Automation Conference (DAC). San Francisco: Institute of Electrical and Electronics Engineers, 2020. p. 1-6. ISBN: 978-1-4503-6725-7.
Czech title
ApproxFPGAs:Transformace přibližných aritmetických komponent navržených pro ASIC na FPGA technologii
Type
conference paper
Language
English
Authors
URL
Keywords

Approximate Computing, FPGA, ASIC, Adder, Multiplier, Arithmetic Units, Machine Learning 

Abstract

There has been abundant research on the development of Approximate Circuits (ACs) for ASICs. However, previous studies have illustrated that ASIC-based ACs offer asymmetrical gains in FPGA-based accelerators. Therefore, an AC that might be pareto-optimal for ASICs might not be pareto-optimal for FPGAs. In this work, we present the ApproxFPGAs methodology that uses machine learning models to reduce the exploration time for analyzing the state-of-the-art ASIC-based ACs to determine the set of pareto-optimal FPGA-based ACs. We also perform a case-study to illustrate the benefits obtained by deploying these pareto-optimal FPGA-ACs in a state-of-the-art automation framework to systematically generate pareto-optimal approximate accelerators that can be deployed in FPGA-based systems to achieve high performance or low-power consumption.

Published
2020
Pages
1–6
Proceedings
2020 57th ACM/IEEE Design Automation Conference (DAC)
ISBN
978-1-4503-6725-7
Publisher
Institute of Electrical and Electronics Engineers
Place
San Francisco
DOI
UT WoS
000628528400045
EID Scopus
BibTeX
@inproceedings{BUT168121,
  author="PRABAKARAN, B. and MRÁZEK, V. and VAŠÍČEK, Z. and SEKANINA, L. and SHAFIQUE, M.",
  title="ApproxFPGAs: Embracing ASIC-based Approximate Arithmetic Components for FPGA-Based Systems",
  booktitle="2020 57th ACM/IEEE Design Automation Conference (DAC)",
  year="2020",
  pages="1--6",
  publisher="Institute of Electrical and Electronics Engineers",
  address="San Francisco",
  doi="10.1109/DAC18072.2020.9218533",
  isbn="978-1-4503-6725-7",
  url="https://arxiv.org/abs/2004.10502"
}
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