FPGA-Based Processor Acceleration for Image Processing Applications

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.

Aceleração de hardware em sistemas embarcados para aprendizado de máquina utilizando KNN em FPGA

“…Com um FPGA Zilinx operando a 337 Mhz máquina Siddiqui et al [2019] mostraram um ganho de 8 vezes em clusterização e 9,6 vezes em reconhecimento de imagens utilizando aprendizado de máquina pelo método K-means em comparação a um processador ARM equivalente. O mesmo apresentou uma eficiência energética 1,7 vezes maior que outras arquiteturas equivalentes (ARM Cortex-A7 CPU, nVIDIA GeForce GTX980 GPU e ARM Mali-T628).…”

Image Processing Using FPGAs

“…Programming an FPGA to accelerate complex algorithms is difficult, with one of four approaches commonly used [1]:…”

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

“…Field programmable gates arrays [28] and graphics processing unit-based parallel approaches are mostly used to decrease computation time [29]. A GPU-based implementation of CMI algorithms was presented for breast cancer detection [30].…”