Publication detail

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

PRABAKARAN, B. MRÁZEK, V. VAŠÍČEK, Z. SEKANINA, L. SHAFIQUE, M.

Original Title

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

Type

conference paper

Language

English

Original 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.

Keywords

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

Authors

PRABAKARAN, B.; MRÁZEK, V.; VAŠÍČEK, Z.; SEKANINA, L.; SHAFIQUE, M.

Released

19. 7. 2020

Publisher

Institute of Electrical and Electronics Engineers

Location

San Francisco

ISBN

978-1-4503-6725-7

Book

2020 57th ACM/IEEE Design Automation Conference (DAC)

Pages from

1

Pages to

6

Pages count

6

URL

https://arxiv.org/abs/2004.10502

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"
}