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KAPow: High-Accuracy, Low-Overhead Online Per-Module Power Estimation for FPGA Designs

Published:09 January 2018Publication History
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Abstract

In an FPGA system-on-chip design, it is often insufficient to merely assess the power consumption of the entire circuit by compile-time estimation or runtime power measurement. Instead, to make better decisions, one must understand the power consumed by each module in the system. In this work, we combine measurements of register-level switching activity and system-level power to build an adaptive online model that produces live breakdowns of power consumption within the design. Online model refinement avoids time-consuming characterization while also allowing the model to track long-term operating condition changes. Central to our method is an automated flow that selects signals predicted to be indicative of high power consumption, instrumenting them for monitoring. We named this technique KAPow, for ‘K’ounting Activity for Power estimation, which we show to be accurate and to have low overheads across a range of representative benchmarks. We also propose a strategy allowing for the identification and subsequent elimination of counters found to be of low significance at runtime, reducing algorithmic complexity without sacrificing significant accuracy. Finally, we demonstrate an application example in which a module-level power breakdown can be used to determine an efficient mapping of tasks to modules and reduce system-wide power consumption by up to 7%.

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                  • Published in

                    cover image ACM Transactions on Reconfigurable Technology and Systems
                    ACM Transactions on Reconfigurable Technology and Systems  Volume 11, Issue 1
                    Special Section on FCCM 2016 and Regular Papers
                    March 2018
                    183 pages
                    ISSN:1936-7406
                    EISSN:1936-7414
                    DOI:10.1145/3178391
                    • Editor:
                    • Steve Wilton
                    Issue’s Table of Contents

                    Copyright © 2018 ACM

                    Publisher

                    Association for Computing Machinery

                    New York, NY, United States

                    Publication History

                    • Published: 9 January 2018
                    • Accepted: 1 July 2017
                    • Revised: 1 June 2017
                    • Received: 1 October 2016
                    Published in trets Volume 11, Issue 1

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