A general approach to solving arbitrarily large problems in a fixed size systolic array

Chuang, Henry Y. H.; Chen, L.

doi:10.1109/hicss.1988.11806

Cited by 2 publications

(2 citation statements)

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“…This is particularly critical for systolic computation due to extensive pipelining and skewing of input data arrays. In this section, an efficient partitioning and scheduling method of the EDT problem on our systolic arrays is presented, based on the feedback emulation technique [20] (also called locally parallel globally sequential mapping [21]). Suppose the systolic array is q wide and the image is n wide.…”

Section: Solving Large Edt Problems In Fixed-size Systolic Arraymentioning

confidence: 99%

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Designing systolic architectures for complete Euclidean distance transform

Chen

Chuang

1995

Journal of VLSI Signal Processing

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Abstract. The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to the Euclidean distance to the nearest foreground pixel. It has important uses in image analysis, computer vision and robotics, and so its VLSI implementation is very useful. In this paper, a sequential algorithm which does not require global operations is first presented. We then apply a sequence of algorithm transforms to convert it into one directly realizable as a square shaped systolic array, and a triangular shaped array after folding transformation. For an n x n image on an equal-sized systolic array, the computation consists of a forward scan and a reverse scan with O (n) time complexity. For images larger than the systolic array, an efficient partitioning and scheduling method based on array emulation is also given.

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Section: Solving Large Edt Problems In Fixed-size Systolic Arraymentioning

confidence: 99%

“…Our systolic arrays can also solve large problems efficiently. In section 4, we show how to partition a large image and schedule the partitions for computation on fixed size systolic arrays by using the array emulation technique [20]. Finally, we analyze the computing times for both small and large problems in section 5.…”

Section: Introductionmentioning

confidence: 99%