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Hardware Acceleration of Image Registration Algorithm on FPGA-based Systems on Chip

Published: 05 May 2019 Publication History
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  • Abstract

    Image processing algorithms are dominating contemporary digital systems due to their importance and adoption by a large number of application domains. Despite their significance, their computational requirements often limit their usage, especially in deeply embedded designs. Heterogeneous computing systems offer a promising solution for this performance gap, leading to their ever increasing utilization by designers. This work targets the acceleration of an image registration pipeline on a System-on-Chip (SoC) including both general purpose and re-configurable computing elements. The evaluation of our proposed HW/SW co-designed image registration application on a state-of-the-art FPGA based SoC showcases its ability to outperform software designs leading to orders of performance speedup (up to 67x) against embedded CPUs.

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    Cited By

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    • (2023) Hephaestus: Codesigning and Automating 3D Image Registration on Reconfigurable ArchitecturesACM Transactions on Embedded Computing Systems10.1145/360792822:5s(1-24)Online publication date: 31-Oct-2023
    • (2023)Faber: A Hardware/SoftWare Toolchain for Image RegistrationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.321889834:1(291-303)Online publication date: 1-Jan-2023
    • (2023)Starlight: A kernel optimizer for GPU processingJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104832(104832)Online publication date: Dec-2023
    • Show More Cited By

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    cover image ACM Other conferences
    COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems
    May 2019
    241 pages
    ISBN:9781450366403
    DOI:10.1145/3312614
    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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    New York, NY, United States

    Publication History

    Published: 05 May 2019

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    Author Tags

    1. Affine Transformation
    2. Correlation Similarity Metric
    3. Downhill Simplex
    4. Image Registration
    5. Zynq

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    View all
    • (2023) Hephaestus: Codesigning and Automating 3D Image Registration on Reconfigurable ArchitecturesACM Transactions on Embedded Computing Systems10.1145/360792822:5s(1-24)Online publication date: 31-Oct-2023
    • (2023)Faber: A Hardware/SoftWare Toolchain for Image RegistrationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.321889834:1(291-303)Online publication date: 1-Jan-2023
    • (2023)Starlight: A kernel optimizer for GPU processingJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104832(104832)Online publication date: Dec-2023
    • (2023)Design and Implementation of Image Sensor Data Capture Based on FPGASN Computer Science10.1007/s42979-023-02433-55:1Online publication date: 8-Dec-2023
    • (2021)A Framework for Customizable FPGA-based Image Registration AcceleratorsThe 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays10.1145/3431920.3439291(251-261)Online publication date: 17-Feb-2021
    • (2021)Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and NetworksSN Computer Science10.1007/s42979-020-00404-82:1Online publication date: 8-Jan-2021
    • (2019)An FPGA-based implementation of Fourier-Mellin Transform2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)10.1109/ICSIDP47821.2019.9173044(1-6)Online publication date: Dec-2019

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