Highly scalable interconnection network for parallel image processing

Hongyu Wang; Weikang Gu

doi:10.1117/12.441679

20 September 2001 Highly scalable interconnection network for parallel image processing

Hongyu Wang, Weikang Gu

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441679
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

In this paper, we introduce a new hierarchical interconnection network for massively parallel systems, named Fully Connected Cubic Network (FCCN). FCCN is able to emulate the popular Hypercube. FCCN has a constant nodal degree of 4 and it therefore eliminates the problem of large fanout in Hypercube. Moreover, the constant degree is an important requirement for efficiently fabricating an architecture in parallel image processing. FCCN is also a highly scalable architecture in that the existing links remain intact when new nodes are introduced. FCCN is maximally fault tolerant, and it enjoys reasonably low diameter, growth of the number of links and average internodal distance. At last, FCCN is used for parallel image processing system for interconnection. The computation results show that FCCN is a high efficient interconnection network for parallel image processing.

Citation Download Citation

Hongyu Wang and Weikang Gu "Highly scalable interconnection network for parallel image processing", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441679

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KEYWORDS

Image processing

Hough transforms

Detection and tracking algorithms

Indium

Network architectures

Parallel processing

Electronics engineering

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