Mapping image processing operations onto a linear systolic machine

A high-performance systolic machine, called Warp, is operational at Carnegie Mellon. The machine has a programmable systolic array of linearly connected cells, each capable of performing 10 million floating-point operations per second. Many image processing operations have been programmed on the machine. This programming experience has yielded new insights in the mapping of image processing operations onto a parallel computer. This paper identifies three major mapping methods that are particularly suited to a Warp-like parallel machine using a linear array of processing elements. These mapping methods correspond to partitioning of input dataset, partitioning of output dataset, and partitioning of computation along the time domain (pipelining). Parallel implementations of several important image processing operations are presented to illustrate the mapping methods. These operations include the Fast Fourier transform (FFT), connected component labelling, Hough transform image warping and relaxation.

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