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Performance comparison of FPGA, GPU and CPU in image processing

Shuichi Asano, Tsutomu Maruyama, Yoshiki Yamaguchi
Syst. & Inf. Eng., Univ. of Tsukuba, Tsukuba, Japan
2009 International Conference on Field Programmable Logic and Applications (2009) Publisher: IEEE, Pages: 126-131

@conference{asano2009performance,

   title={Performance comparison of FPGA, GPU and CPU in image processing},

   author={Asano, S. and Maruyama, T. and Yamaguchi, Y.},

   booktitle={Field Programmable Logic and Applications, 2009. FPL 2009. International Conference on},

   pages={126–131},

   issn={1946-1488},

   year={2009},

   organization={IEEE}

}

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Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve high performance. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. However, the cores are grouped, and data transfer between the groups is very limited. Programming tools for FPGA, SIMD instructions on CPU and a large number of cores on GPU have been developed, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of FPGA, GPU and CPU using three applications in image processing; two-dimensional filters, stereo-vision and k-means clustering, and make it clear which platform is faster under which conditions.
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