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 :
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