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Adaptive Selection and Clustering of Partial Reconfiguration Modules for Modern FPGA Design Flow

Published:02 April 2023Publication History
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Abstract

Dynamic Partially Reconfiguration (DPR) on FPGA has attracted significant research interest in recent years since it provides benefits such as reduced area and flexible functionality. However, due to the lack of supporting synthesis tools in the current DPR design flow, leveraging benefits from DPR requires specific design expertise with laborious manual design effort. Considering the complicated concurrency relations among various functions, it is challenging to select appropriate Partial Reconfiguration Modules (PR Modules) and cluster them into proper groups with a proper reconfiguration schedule so that the hardware modules can be swapped in and out correctly during the run time. Furthermore, the design of PR Modules also impacts reconfiguration latency and resource utilization greatly. In this paper, we propose a Maximum-Weight Independent Set model to formulate the PR Module selection and clustering problem so that the original manual exploration can be solved efficiently and automatically. We also propose a step-wise adjustment configuration prefetching strategy incorporated in our model to generate optimized reconfiguration schedules. Our proposed approach not only supports various design constraints but also can consider multiple objectives such as area and reconfiguration delay. Experimental results show that our approach can optimize resource utilization and reduce reconfiguration delay with good scalability. Especially, the implementation of the real design case shows that our approach can be embedded in Xilinx's DPR design flow successfully.

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  1. Adaptive Selection and Clustering of Partial Reconfiguration Modules for Modern FPGA Design Flow

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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 2
      June 2023
      451 pages
      ISSN:1936-7406
      EISSN:1936-7414
      DOI:10.1145/3587031
      • 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: 2 April 2023
      • Online AM: 10 October 2022
      • Accepted: 26 September 2022
      • Revised: 20 August 2022
      • Received: 19 May 2022
      Published in trets Volume 16, Issue 2

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