DocumentCode
2265792
Title
VLSI implementation of a Cellular Neural Network with programmable control operator
Author
Cardarilli, G.C. ; Lojacono, R. ; Salerno, M. ; Sargeni, F.
Author_Institution
Dept. of Electron. Eng., Rome Univ., Italy
fYear
1993
fDate
16-18 Aug 1993
Firstpage
1089
Abstract
Cellular Neural Networks (CNN) are a particular class of neural networks based on a regular structure. Using this property a suitable architecture can be designed for a very efficient analog implementation. The core of a cellular neuron is an analog multiplier that can be implemented by using different approaches. In particular, if the CNN is used for conventional applications, as for example Connected Component Detector (CCD), different solutions are possible in terms of fixed or programmable cloning template. In this paper a VLSI implementation of programmable CNN with control operator B and symmetrical and anti symmetrical feedback operator A is presented
Keywords
VLSI; analogue multipliers; cellular neural nets; neural chips; recurrent neural nets; CNN; VLSI implementation; analog implementation; analog multiplier; cellular neural network; cloning template; connected component detector; feedback operator; programmable control operator; regular structure; Cellular neural networks; Charge coupled devices; Cloning; Detectors; Neural networks; Neurofeedback; Neurons; Programmable control; Very large scale integration; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location
Detroit, MI
Print_ISBN
0-7803-1760-2
Type
conf
DOI
10.1109/MWSCAS.1993.343274
Filename
343274
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