DocumentCode
2662038
Title
VLSI implementation of cellular neural networks
Author
Yang, L. ; Chua, L.O. ; Krieg, K.R.
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
fYear
1990
fDate
1-3 May 1990
Firstpage
2425
Abstract
A cellular neural network (CNN) which is an example of very-large-scale analog processing or collective analog computation is presented. The CNN architecture combines some features of fully connected analog neural networks with the nearest-neighbor interactions found in cellular automata. VLSI implementation of these circuits is discussed. Though the circuits described have been fabricated for noise removal and connected segment extraction, most of the features of the VLSI circuits are shared by VLSI implementations of other processing functions
Keywords
VLSI; analogue computer circuits; computer architecture; neural nets; pattern recognition; VLSI implementation; cellular neural networks; collective analog computation; connected segment extraction; fully connected analog neural networks; nearest-neighbor interactions; noise removal; very-large-scale analog processing; Analog computers; Cellular neural networks; Circuit noise; Computer architecture; Fabrication; Feedback circuits; Force feedback; Large-scale systems; Neural networks; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location
New Orleans, LA
Type
conf
DOI
10.1109/ISCAS.1990.112500
Filename
112500
Link To Document