Keywords
artificial neural networks, interconnect technology, multi-chip modules
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.
Original Publication Citation
Stout, M., Salmon, L., Rudolph, G., and Martinez, T. R., "A Multi-Chip Module Implementation of a Neural Network", Proceedings of the IEEE Multi-Chip Module Conference MCMC-94, pp. 2-25, 1994.
BYU ScholarsArchive Citation
Martinez, Tony R.; Rudolph, George L.; Salmon, Linton G.; and Stout, Matthew G., "A Multi-Chip Module Implementation of a Neural Network" (1994). Faculty Publications. 1166.
https://scholarsarchive.byu.edu/facpub/1166
Document Type
Peer-Reviewed Article
Publication Date
1994-03-17
Permanent URL
http://hdl.lib.byu.edu/1877/2408
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 1994 Institute of Electrical and Electronics Engineers. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/