DocumentCode :
1062222
Title :
Piecewise-exponential approximation for fast time-domain simulation of 2-D cellular neural networks
Author :
De Sandre, Guido ; Premoli, Amedeo
Author_Institution :
SGS-Thomson Microelectron., Agrate Brianza, Italy
Volume :
51
Issue :
8
fYear :
2004
Firstpage :
400
Lastpage :
405
Abstract :
Cellular neural networks (CNNs) were introduced as promising image processing systems. However, since analytical design techniques are rarely available, extensive simulation is the main practical tool for developing significant applications. This paper presents a new algorithm for fast simulation of large-scale CNNs. It is based on the discretization of the sigmoid generating the output from the state of each cell. This discretization leads to a piecewise exponential approximation of the time-domain solution. Computation is only required when the output of a cell jumps to a different discrete level and involves only this cell and its neighbors. The algorithm is spatially adaptive since the computational effort is concentrated on the most rapidly evolving portions of the array.
Keywords :
cellular neural nets; exponential distribution; piecewise linear techniques; time-domain analysis; 2D cellular neural networks; adaptive computation; fast time-domain simulation; image processing systems; piecewise-exponential approximation; Adaptive arrays; Analytical models; Cellular neural networks; Computational modeling; Computer networks; Helium; Image processing; Large-scale systems; Neural networks; Time domain analysis; Adaptive computation; CNNs; cellular neural networks; fast simulation of large arrays; piecewise exponential approximation;
fLanguage :
English
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
Publisher :
ieee
ISSN :
1549-7747
Type :
jour
DOI :
10.1109/TCSII.2004.832768
Filename :
1323222
Link To Document :
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