• 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