• 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