MGAP在机器感知中的应用

Heung-Nam Kim, M. J. Irwin, R. Owens
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引用次数: 16

摘要

由于机器感知中的图像处理任务是计算密集型的,因此通常采用大规模并行处理来利用其固有的并行性。一些开创性的研究人员已经开发了大规模并行处理系统,包括CLIP家族,MPP和CM。然而,由于它们的昂贵和物理尺寸,所有这些系统都很难在机器感知系统中使用。为了解决这些问题,我们开发了一种廉价且易于移植的大规模并行处理系统,MGAP(微粒度阵列处理器)。MGAP是一种非常细粒度、大规模并行、可编程阵列处理器,设计用于桌面工作站中的低成本大规模并行协处理器板。我们讨论了MGAP在图像处理子任务中的多种应用,包括有效的直方图算法、收缩二维离散余弦变换(DCT)和动态空间扭曲算法(DSWA)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MGAP applications in machine perception
Since image processing tasks in machine perception are computationally intensive, massively parallel processing is often employed to exploit the inherent parallelism. Some pioneering researchers have developed massively parallel processing systems including the CLIP family, the MPP, and the CM. All of these systems however, are hard to use in machine perception systems because of their expensiveness and physical size. To tackle these problems we developed an inexpensive and easily portable massively parallel processing system, the MGAP (Micro Grained Array Processor). The MGAP is a very fine grained, massively parallel, programmable array processor which is designed to be used as a low cost massively parallel coprocessor board in a desktop workstation. We discuss the versatile applications of the MGAP for image processing subtasks including an efficient histogramming algorithm, the systolic 2D Discrete Cosine Transform (DCT), and the Dynamic Space Warping Algorithm (DSWA).
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