Pipelining Gauss Seidel Method for Analysis of Discrete Time Cellular Neural Networks

Naohiko SHIMIZU  Gui-Xin CHENG  Munemitsu IKEGAMI  Yoshinori NAKAMURA  Mamoru TANAKA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E77-A   No.8   pp.1396-1403
Publication Date: 1994/08/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
cellular neural networks,  dynamics,  numerical analysis,  relaxation method,  pipelining,  image coding,  image decoding,  structural compression,  regularization,  communication system,  

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This paper describes a pipelining universal system of discrete time cellular neural networks (DTCNNs). The new relaxation-based algorithm which is called a Pipelining Gauss Seidel (PGS) method is used to solve the CNN state equations in pipelining. In the systolic system of N processor elements {PEi}, each PEi performs the convolusional computation (CC) of all cells and the preceding PEi-1 performs the CC of all cells taking precedence over it by the precedence interval number p. The expected maximum number of PE's for the speeding up is given by n/p where n means the number of cells. For its application, the encoding and decoding process of moving images is simulated.