Analog signal processing using cellular neural networks

Abstract

The cellular neural network (CNN) is an example of very-large-scale analog processing or collective analog computation. The CNN architecture combines some features of fully connected analog neural networks with the nearest-neighbor interactions found in cellular automata. These networks have numerous advantages both for simulation and for VLSI implementation and can perform (though are not limited to) several important image processing functions. The important features which enable the CNN architecture to perform signal processing functions using standard VLSI technology are discussed. Circuit characteristics are outlined, and examples of cellular neural network signal processing are presented. Connected segment extraction is illustrated by examples, as is histogramming using a two-layer CNN.<>