DocumentCode :
2848631
Title :
Parallel architectures for artificial neural nets
Author :
King, S.Y.
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ
fYear :
1988
fDate :
25-27 May 1988
Firstpage :
163
Lastpage :
174
Abstract :
The key aspects of the modeling, algorithm, and architecture for artificial neural nets (ANNs) are reviewed. A programmable systolic array meant for a variety of connectivity patterns for ANNs is proposed. Considered in the design are both the search and learning phases of a class of ANNs. A system-theoretic approach is adopted to elucidate modeling issues for ANNs. On the basis the issues of expressibility and discrimination, fault tolerance and generalization, size of hidden units/layers, interconnectivity patterns, and circuit model for analog ANN implementations are addressed
Keywords :
artificial intelligence; cellular arrays; neural nets; parallel architectures; artificial neural nets; circuit model; discrimination; expressibility; fault tolerance; interconnectivity patterns; learning phases; modeling; parallel architectures; programmable systolic array; search; Artificial neural networks; Biological neural networks; Brain modeling; Circuits; Humans; Neurons; Parallel architectures; Retina; Switches; Systolic arrays;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systolic Arrays, 1988., Proceedings of the International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-8186-8860-2
Type :
conf
DOI :
10.1109/ARRAYS.1988.18057
Filename :
18057
Link To Document :
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