Implementation of cellular neural networks with cloning templates of smaller dimensions

Author

Akbari-Dilmaghani, Rahim ; Taylor, John

Author_Institution

Dept. of Electron. & Electr. Eng., Univ. Coll. London, UK

Volume

1

fYear

1996

fDate

13-16 Oct 1996

Firstpage

410

Abstract

A new approach to the implementation of cellular neural networks (CNNs) with cloning templates of smaller dimensions is presented. The method is based on the assumptions that the circuit transients are short and possibly monotonic, and that the values of the initial state variables are taken into consideration in the design. Using the proposed method we can reduce the size of A template with any dimension (r⩾1) into a single element a (ij, ij) which results in a significant reduction in the circuit complexity of a VLSI implementation of CNNs. Simulation results are presented to confirm the viability of the proposed method

Keywords

VLSI; cellular neural nets; neural chips; CNN implementation; VLSI implementation; cellular neural networks; circuit complexity reduction; cloning templates; initial state variables; Algorithm design and analysis; Cellular neural networks; Circuit simulation; Cloning; Complexity theory; Educational institutions; Neurons; Signal processing; Space technology; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits, and Systems, 1996. ICECS '96., Proceedings of the Third IEEE International Conference on

Conference_Location

Rodos

Print_ISBN

0-7803-3650-X

Type

conf

DOI

10.1109/ICECS.1996.582859

Filename

582859