Mixed signal neural circuits for shortest path computation

Author

Shaikh-Husin, N. ; Meador, Jack L.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA

Volume

2

fYear

1995

fDate

Oct. 30 1995-Nov. 1 1995

Firstpage

876

Abstract

The objective of the graphical shortest path problem is to discover the least cost path in a weighted graph between a given source vertex and one or more destinations. This problem class has numerous practical applications including data network routing and speech recognition. This paper discusses the hardware realization of a recurrent spatiotemporal neural network for single source multiple-destination graphical shortest path problems. The network exhibits a regular interconnect structure and uses simple processing units in a combination which is well suited for VLSI implementation with a standard fabrication process.

Keywords

neural chips; VLSI; data network routing; destinations; graphical shortest path problem; hardware realization; least cost path; mixed signal neural circuits; processing units; recurrent spatiotemporal neural network; regular interconnect structure; shortest path computation; source vertex; speech recognition; standard fabrication process; weighted graph; Costs; Integrated circuit interconnections; Neural network hardware; Neural networks; Recurrent neural networks; Routing; Shortest path problem; Spatiotemporal phenomena; Speech recognition; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1995. 1995 Conference Record of the Twenty-Ninth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-8186-7370-2

Type

conf

DOI

10.1109/ACSSC.1995.540825

Filename

540825