FPGA-Based Processor Acceleration for Image Processing Applications

Fahad Siddiqui, Sam Amiri, Umar Ibrahim Minhas, Tiantai Deng, Roger Woods, Karen Rafferty, Daniel Crookes

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)
    80 Downloads (Pure)

    Abstract

    FPGA-based embedded image processing systems offer considerable computing resources but present programming challenges when compared to software systems. The paper describes an approach based on an FPGA-based soft processor called Image Processing Processor (IPPro) which can operate up to 337 MHz on a high-end Xilinx FPGA family and gives details of the dataflow-based programming environment. The approach is demonstrated for a k-means clustering operation and a traffic sign recognition application, both of which have been prototyped on an Avnet Zedboard that has Xilinx Zynq-7000 system-on-chip (SoC). A number of parallel dataflow mapping options were explored giving a speed-up of 8 times for the k-means clustering using 16 IPPro cores, and a speed-up of 9.6 times for the morphology filter operation of the traffic sign recognition using 16 IPPro cores compared to their equivalent ARM-based software implementations. We show that for k-means clustering, the 16 IPPro cores implementation is 57, 28 and 1.7 times more power efficient (fps/W) than ARM Cortex-A7 CPU, nVIDIA GeForce GTX980 GPU and ARM Mali-T628 embedded GPU respectively.
    Original languageEnglish
    Article number16
    Number of pages22
    JournalJournal of Imaging
    Volume5
    Issue number1
    DOIs
    Publication statusPublished - 13 Jan 2019

    Bibliographical note

    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

    Keywords

    • FPGA
    • Hardware acceleration
    • Heterogeneous computing
    • Image processing
    • Processor architectures

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

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