DocumentCode
768333
Title
An hierarchical VLSI neural network architecture
Author
Mason, R. ; Robertson, W. ; Pincock, D.
Author_Institution
Tech. Univ. of Nova Scotia, Halifax, NS, Canada
Volume
27
Issue
1
fYear
1992
fDate
1/1/1992 12:00:00 AM
Firstpage
106
Lastpage
108
Abstract
As neural network systems are scaled up in size it will become extremely difficult, if not impossible, to maintain full connectivity. A digital architecture which exhibits hierarchical connectivity similar to that observed in many biological neural networks is described. At the lowest level, clusters of fully connected neurons correspond to subnetworks. These subnetworks are then sparsely connected to form the complete neural network system. The architecture exploits the inherent density and large bandwidth of on-chip RAM and can use either a large number of bit-serial processors or a reduced number of bit-parallel processors. A prototype chip which implements a complete subnetwork has been fabricated in 3-μm CMOS and is fully functional
Keywords
CMOS integrated circuits; VLSI; neural nets; parallel architectures; 3 micron; CMOS; VLSI neural network architecture; bit-parallel processors; bit-serial processors; clusters of fully connected neurons; complete subnetwork; digital architecture; hierarchical connectivity; hierarchical neural network architecture; large bandwidth; on-chip RAM; prototype chip; sparsely connected subnetworks; Artificial neural networks; Bandwidth; Biological neural networks; CMOS technology; Neural networks; Neurons; Prototypes; Random access memory; Semiconductor device modeling; Very large scale integration;
fLanguage
English
Journal_Title
Solid-State Circuits, IEEE Journal of
Publisher
ieee
ISSN
0018-9200
Type
jour
DOI
10.1109/4.109562
Filename
109562
Link To Document