• DocumentCode
    2265792
  • Title

    VLSI implementation of a Cellular Neural Network with programmable control operator

  • Author

    Cardarilli, G.C. ; Lojacono, R. ; Salerno, M. ; Sargeni, F.

  • Author_Institution
    Dept. of Electron. Eng., Rome Univ., Italy
  • fYear
    1993
  • fDate
    16-18 Aug 1993
  • Firstpage
    1089
  • Abstract
    Cellular Neural Networks (CNN) are a particular class of neural networks based on a regular structure. Using this property a suitable architecture can be designed for a very efficient analog implementation. The core of a cellular neuron is an analog multiplier that can be implemented by using different approaches. In particular, if the CNN is used for conventional applications, as for example Connected Component Detector (CCD), different solutions are possible in terms of fixed or programmable cloning template. In this paper a VLSI implementation of programmable CNN with control operator B and symmetrical and anti symmetrical feedback operator A is presented
  • Keywords
    VLSI; analogue multipliers; cellular neural nets; neural chips; recurrent neural nets; CNN; VLSI implementation; analog implementation; analog multiplier; cellular neural network; cloning template; connected component detector; feedback operator; programmable control operator; regular structure; Cellular neural networks; Charge coupled devices; Cloning; Detectors; Neural networks; Neurofeedback; Neurons; Programmable control; Very large scale integration; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
  • Conference_Location
    Detroit, MI
  • Print_ISBN
    0-7803-1760-2
  • Type

    conf

  • DOI
    10.1109/MWSCAS.1993.343274
  • Filename
    343274