Title :
Input/output hardware strategies for cellular neural networks
Author :
Kinget, Peter ; Steyaert, Michiel
Author_Institution :
ESAT-MICAS, Katholieke Univ., Leuven, Heverlee, Belgium
fDate :
27 Jun-2 Jul 1994
Abstract :
In this paper hardware alternatives for the input/output circuits of a cellular neural network are discussed. Cellular neural networks are primarily applied in image processing applications. The realisation of a fully programmable 128×128 cellular neural network is possible in a standard state of the art CMOS process. Moreover, several alternatives for the integration of a solid-state image sensor that are compatible with a standard CMOS process have been published. The different possible input/output structures are evaluated towards their compatibility with both techniques. They provide the link towards the realisation of a smart image sensor and processor based on cellular neural networks
Keywords :
CMOS integrated circuits; cellular neural nets; image sensors; neural chips; optical neural nets; CMOS process; cellular neural networks; fully programmable 128×128 cellular neural network; image processing; input/output circuits; input/output hardware strategies; smart image sensor; solid-state image sensor; CMOS image sensors; CMOS process; CMOS technology; Cellular neural networks; Image processing; Image sensors; Integrated circuit interconnections; Neural network hardware; Solid state circuits; Very large scale integration;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374449
