Date

February 1990

Document Type

Thesis

Degree Name

M.S.

Department

Dept. of Computer Science and Engineering

Institution

Oregon Graduate Institute of Science & Technology

Abstract

Before artificial neural network applications become common there must be inexpensive hardware that will allow large networks to be run in real time. It is uncertain how large networks will do when constrained to implementations on architectures of current technology. Some tradeoffs must be made when the network models are implemented efficiently. Three popular artificial neural network models are analyzed. This paper discusses the effects on performance when the models are modified for efficient hardware implementation.

Identifier

doi:10.6083/M4251G44

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