• DocumentCode
    2757136
  • Title

    Neural network architectures for systolic arrays

  • Author

    Shim, Chongjoon ; Cheung, John Y.

  • Author_Institution
    Sch. of Electr. Eng. & Comput. Sci., Oklahoma Univ., Norman, OK, USA
  • fYear
    1991
  • fDate
    8-14 Jul 1991
  • Abstract
    Summary form only given, as follows. The authors propose the use of an artificial neural network model to simulate the functions and operations of a systolic array. A systolic array and a neural network are both easy to implement and easy to configure. Since a neural network can be implemented on a programmable VLSI chip, it is very fast, easy to reconfigure, and cost-effective. It is concluded that a wide variety of designs of systolic arrays can easily be simulated on neural networks
  • Keywords
    VLSI; neural nets; systolic arrays; cost effectiveness; neural network architecture; programmable VLSI chip; reconfiguration; simulation; systolic arrays; Artificial neural networks; Associative memory; Computational modeling; Computer architecture; Computer networks; Computer science; Computer simulation; Neural networks; Systolic arrays; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-0164-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.155694
  • Filename
    155694