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首页> 外文会议>2015 IEEE Symposium on Computer Applications amp; Industrial Electronics >A quantitative cross-architecture study of morphological image processing on CPUs, GPUs, and FPGAs
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A quantitative cross-architecture study of morphological image processing on CPUs, GPUs, and FPGAs

机译：在CPU，GPU和FPGA上进行形态图像处理的定量跨体系结构研究

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摘要

The rapidly growing applications based on morphological operations in image processing and computer vision make efficient implementations of these key blocks an important topic of research. Nevertheless, a detailed comparison of the energy efficiency and performance of these implementations that covers all available major hardware platforms is still missing. In this paper we evaluate the performance and power consumption of the most efficient available morphological image processing algorithms for CPU, GPU, and FPGA platforms in detail. In addition, we study the suitability of available morphological library units for high-level synthesis and compare the results with an optimized hand-coded FPGA implementation. We demonstrate that even high-end GPUs cannot achieve the throughputs of modern CPUs and FPGAs by far. Our experimental results show that an FPGA implementation is 8-10 times more energy efficient for this application, being comparable in speed to CPUs for large kernels.
机译：在图像处理和计算机视觉中基于形态运算的快速增长的应用程序使这些关键模块的有效实现成为研究的重要课题。但是，仍然缺少对这些实现的能源效率和性能的详细比较，这些比较涵盖所有可用的主要硬件平台。在本文中，我们详细评估了适用于CPU，GPU和FPGA平台的最有效的形态图像处理算法的性能和功耗。此外，我们研究了可用的形态学库单元用于高级合成的适用性，并将结果与​​优化的手动编码FPGA实现进行了比较。我们证明，即使高端GPU也无法实现现代CPU和FPGA的吞吐量。我们的实验结果表明，对于该应用，FPGA实施的能源效率高8-10倍，其速度与大型内核的CPU相当。

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