Area efficient implementations of fixed-template CNN's

Implementations of fixed-template Cellular Neural Networks (CNN's) with reduced circuit complexity are presented, Considerable improvements in area without performance degradation have been obtained by: 1) using single-polarity signals that reduce the number of transistors required for signal replication and to generate the pseudo-linear output function; 2) using simple current-mode circuits to implement the output pseudo-linear function; and 3) searching for network parameter configurations that solve a particular application using the proposed circuit implementation with less hardware complexity. Experimental results for a CCD-CNN chip prototype with a density of 230 cells per millimeter-squared (mm(2)) are also reported.