FPGA-Based Processor Acceleration for Image Processing Applications
AbstractFPGA-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.
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Siddiqui, F.; Amiri, S.; Minhas, U.I.; Deng, T.; Woods, R.; Rafferty, K.; Crookes, D. FPGA-Based Processor Acceleration for Image Processing Applications. J. Imaging 2019, 5, 16.
Siddiqui F, Amiri S, Minhas UI, Deng T, Woods R, Rafferty K, Crookes D. FPGA-Based Processor Acceleration for Image Processing Applications. Journal of Imaging. 2019; 5(1):16.Chicago/Turabian Style
Siddiqui, Fahad; Amiri, Sam; Minhas, Umar I.; Deng, Tiantai; Woods, Roger; Rafferty, Karen; Crookes, Daniel. 2019. "FPGA-Based Processor Acceleration for Image Processing Applications." J. Imaging 5, no. 1: 16.
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