Title :
Extended CNN topologies and VLSI implementations
Author :
Bernardo, G. Di ; Lavorgna, M. ; Occhipinti, L.
Author_Institution :
Soft Comput. Group, STMicroelectron., Catania, Italy
Abstract :
Summary form only given. The work carried out is oriented to extend classical topologies of Cellular Neural Networks (CNN) to more general cellular processors, and their implementation, in order to build universal machines able to perform complex tasks and nonlinear dynamic generation. More precisely, State-Controlled CNNs and Fuzzy Cellular Processors are briefly introduced. The former are implemented in mixed-signal CMOS technology
Keywords :
CMOS integrated circuits; VLSI; cellular neural nets; fuzzy neural nets; mixed analogue-digital integrated circuits; neural chips; VLSI implementations; cellular neural networks; extended CNN topologies; fuzzy cellular processors; mixed-signal CMOS technology; nonlinear dynamic generation; state-controlled CNNs; universal machines; CMOS technology; Cellular networks; Cellular neural networks; Chaos; Circuit topology; Integrated circuit interconnections; Multi-layer neural network; Network topology; Neural networks; Very large scale integration;
Conference_Titel :
Electronics, Circuits and Systems, 1998 IEEE International Conference on
Conference_Location :
Lisboa
Print_ISBN :
0-7803-5008-1
DOI :
10.1109/ICECS.1998.813299
