DocumentCode :
1875288
Title :
A multi-chip module implementation of a neural network
Author :
Stout, Matthew G. ; Salmo, Linton G. ; Rudolph, George L. ; Martinez, Tony R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA
fYear :
1994
fDate :
15-17 Mar 1994
Firstpage :
20
Lastpage :
25
Abstract :
The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models
Keywords :
learning (artificial intelligence); multichip modules; neural chips; parallel architectures; self-organising feature maps; MCM technology; artificial neural network; connectionist models; high-density interconnect technologies; multi-chip module implementation; parallel architecture; self-organizing parallel dynamic learning model; Artificial neural networks; Computational modeling; Computer networks; Computer simulation; Equations; Neural network hardware; Neural networks; Neurons; Parallel architectures; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multi-Chip Module Conference, 1994. MCMC-94, Proceedings., 1994 IEEE
Conference_Location :
Santa Cruz, CA
Print_ISBN :
0-8186-5560-7
Type :
conf
DOI :
10.1109/MCMC.1994.292532
Filename :
292532
Link To Document :
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