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CGRA-EAM—Rapid Energy and Area Estimation for Coarse-grained Reconfigurable Architectures

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Published:13 September 2021Publication History
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Abstract

Reconfigurable architectures are quickly gaining in popularity due to their flexibility and ability to provide high energy efficiency. However, reconfigurable systems allow for a huge design space. Iterative design space exploration (DSE) is often required to achieve good Pareto points with respect to some combination of performance, area, and/or energy. DSE tools depend on information about hardware characteristics in these aspects. These characteristics can be obtained from hardware synthesis and net-list simulation, but this is very time-consuming. Therefore, architecture models are common. This work introduces CGRA-EAM (Coarse-Grained Reconfigurable Architecture - Energy & Area Model), a model for energy and area estimation framework for coarse-grained reconfigurable architectures. The model is evaluated for the Blocks CGRA. The results demonstrate that the mean absolute percentage error is 15.5% and 2.1% for energy and area, respectively, while the model achieves a speedup of close to three orders of magnitude compared to synthesis.

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      • Published in

        cover image ACM Transactions on Reconfigurable Technology and Systems
        ACM Transactions on Reconfigurable Technology and Systems  Volume 14, Issue 4
        December 2021
        165 pages
        ISSN:1936-7406
        EISSN:1936-7414
        DOI:10.1145/3483341
        • Editor:
        • Deming Chen
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 September 2021
        • Revised: 1 May 2021
        • Accepted: 1 May 2021
        • Received: 1 December 2020
        Published in trets Volume 14, Issue 4

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