Defect tolerant implementations of feed-forward and recurrent neural networks

Author

Franzon, Paul ; Van den Bout, David ; Paulos, John ; Miller, Thomas, III ; Snyder, Wesley ; Nagle, Troy ; Liu, Wentai

Author_Institution

Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA

fYear

1990

fDate

23-25 Jan 1990

Firstpage

160

Lastpage

166

Abstract

Many of the defect tolerant techniques employed to achieve wafer-scale integration can also be used to construct flexible and scalable architectures. These techniques are applied to two artificial neural networks: a feed-forward analog network with backpropagation and an efficient digital recurrent network

Keywords

VLSI; computer architecture; digital integrated circuits; fault tolerant computing; integrated circuit technology; neural nets; WSI; artificial neural networks; backpropagation; defect tolerant implementations; defect tolerant techniques; digital recurrent network; feed-forward analog network; feed-forward neural networks; recurrent neural networks; scalable architectures; wafer-scale integration; Artificial neural networks; Computer architecture; Feedforward neural networks; Feedforward systems; Feeds; Large-scale systems; Neural networks; Neurons; Recurrent neural networks; Wafer scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Wafer Scale Integration, 1990. Proceedings., [2nd] International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-8186-9013-5

Type

conf

DOI

10.1109/ICWSI.1990.63897

Filename

63897