University of Hertfordshire

From the same journal

By the same authors

  • Yuan Wang
  • Martin Albrecht Trefzer
  • Simon Jonathan Bale
  • James Alfred Walker
  • Andrew Martin Tyrrell
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Original languageEnglish
JournalIET Computers and Digital Techniques
Journal publication date28 Oct 2018
Publication statusAccepted/In press - 28 Oct 2018

Abstract

Circuit clustering algorithms fit synthesised circuits into FPGA configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort required and performance achievable in subsequent place-and-route processes. Circuit clustering is limited by hardware constraints of specific target architectures. Hence, better circuit clustering approaches are essential for improving device utilisation whilst at the same time optimising circuit performance parameters such as, e.g., power and delay. In this paper, we present a method based on multi-objective genetic algorithm (MOGA) to facilitate circuit clustering. We address a number of challenges including CLB input bandwidth constraints, improvement of CLB utilisation, minimisation of interconnects between CLBs. Our new approach has been validated using the "Golden 20" MCNC benchmark circuits that are regularly used in FPGA-related literature. The results show that the method proposed in this paper achieves improvements of up to 50% in clustering, routability and timing when compared to state-of-the-art approaches including VPack, T-VPack, RPack, DPack, HDPack, MOPack and iRAC. Key contribution of this work is a flexible EDA flow that can incorporate numerous objectives required to successfully tackle real-world circuit design on FPGA, providing device utilisation at increased design performance.

Notes

This paper is a postprint of a paper submitted to and accepted for publication in IET Computers and Digital Techniques and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

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