• DocumentCode
    1463738
  • Title

    A new cell output nonlinearity for dense cellular nonlinear network integration

  • Author

    Paasio, Ari ; Halonen, Kari

  • Author_Institution
    Electron. Circuit Design Lab., Helsinki Univ. of Technol., Espoo, Finland
  • Volume
    48
  • Issue
    3
  • fYear
    2001
  • fDate
    3/1/2001 12:00:00 AM
  • Firstpage
    272
  • Lastpage
    280
  • Abstract
    A new approach for designing a cellular nonlinear network (CNN) cell is introduced. The method can be used when the processed images are bipolar, i.e., black and white. A simple circuit realization is introduced that can be applied when building up the analog part of the cell. The cell output nonlinearity is an easily realizable positive range high gain sigmoid. Moreover, a state limited model is adopted to decrease the complexity of the design. The coefficient building blocks are simple, and also because these blocks introduce an almost error free multiplication by zero, the corresponding devices can be made relatively small due to relaxed coefficient accuracy requirements. This approach yields a very small cell area on silicon and can be used with inexpensive digital CMOS processes. Simulation results are given for both static and dynamic behavior of the proposed structure. The dynamic simulation shows very fast convergence time compared to other reported approaches for CNN very large scale integration implementation
  • Keywords
    CMOS analogue integrated circuits; VLSI; cellular neural nets; neural chips; CMOS VLSI; analog circuit; bipolar image processing; cell output nonlinearity; cellular nonlinear network; coefficient multiplier; dynamic simulation; positive range high gain sigmoid; state limited model; static simulation; CMOS process; CMOS technology; Cellular networks; Cellular neural networks; Convergence; Electronic circuits; Helium; Nonlinear equations; Silicon; Very large scale integration;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7122
  • Type

    jour

  • DOI
    10.1109/81.915384
  • Filename
    915384