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Advantages of a Statistical Estimation Approach for Clock Frequency Estimation of Heterogeneous and Irregular CGRAs

Published:22 December 2022Publication History
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Abstract

Estimating the maximum clock frequency of homogeneous Coarse Grained Reconfigurable Arrays/Architectures (CGRAs) with an arbitrary number of Processing Elements (PE) is difficult. Clock frequency estimation of highly heterogeneous CGRAs takes additional factors into account, thus is even more difficult. Main challenges are the heterogeneous set of operators for each Processing Element (PE) and the irregular interconnect (connecting a CGRA’s PEs). Multiple estimation approaches could be reasonable. We propose an optimized statistical estimator, which is based on our prior work. We demonstrate its superiority to state-of-the-art neural networks in terms of accuracy and robustness, especially in situations with a sparse set of training data.

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      • Published in

        cover image ACM Transactions on Reconfigurable Technology and Systems
        ACM Transactions on Reconfigurable Technology and Systems  Volume 16, Issue 1
        March 2023
        403 pages
        ISSN:1936-7406
        EISSN:1936-7414
        DOI:10.1145/35733111
        • Editor:
        • Deming Chen
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 22 December 2022
        • Online AM: 25 April 2022
        • Accepted: 10 April 2022
        • Revised: 30 December 2021
        • Received: 28 August 2021
        Published in trets Volume 16, Issue 1

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