使用GPU和FPGA加速相位相关函数

Kentaro Matsuo, T. Hamada, Masayuki Miyoshi, Yuichiro Shibata, K. Oguri
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引用次数: 6

摘要

在本文中,我们比较研究了用gpu、ASIC和fpga实现相位相关函数。纯相位相关(POC)方法在模式匹配和图像配准方面具有较高的鲁棒性和亚像素精度。但是,由于二维快速傅里叶变换等问题,在计算速度上存在一定的劣势。为了解决计算成本问题,我们提出了一种利用GPU加速POC方法的新方法。使用我们基于GPU的POC实现,使用256 x 256像素的GPU,每个POC计算可以在2.36毫秒内完成,另一方面,Cinderella II 100 MHz(ASIC)在27.15毫秒内完成,Xilinx XC2V6000 66 MHz(FPGA)在4.51毫秒内完成。这些结果表明,对于POC计算和基于fft的计算,gpu在性能和性能数据方面非常有竞争力,而fpga在每频率数据方面具有竞争力。
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Accelerating Phase Correlation Functions Using GPU and FPGA
In this paper, we present a comparison study about implementations of phase correlation function using GPUs, ASIC and FPGAs. The Phase Only Correlation(POC) method demonstrates high robustness and subpixel accuracy in the pattern matching and the image registration. However, there is a disadvantage in computational speed because of the calculation of 2D-FFT etc. We have proposed a novel approach to accelerate POC method using GPU to solve the calculation cost problem. Using our GPU-based POC implementation, each POC calculation can be done within 2.36 milli seconds using a GPU for 256 x 256 pixels, on the other hand, within 27.15 milli seconds for Cinderella II 100 MHz (ASIC), 4.51 milli seconds for Xilinx XC2V6000 66 MHz(FPGA). These results show that, for POC calculation and FFT-based computations in general, GPUs are very competitive in terms of performance and performance figures, whereas FPGAs are competitive in terms of performance per frequency figures.
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