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 millimetersquared (mm²) are also reported