• DocumentCode
    2074211
  • Title

    Neuromorphic CMOL circuits

  • Author

    Likharev, Konstantin K.

  • Author_Institution
    Stony Brook Univ., NY, USA
  • Volume
    1
  • fYear
    2003
  • fDate
    12-14 Aug. 2003
  • Firstpage
    339
  • Abstract
    This is a brief review of the recent work on the development of neuromorphic architectures for future hybrid CMOS/nanowire/MOLecular ("CMOL") circuits. Such circuits may provide the first chance for the implementation of advanced information processing systems with areal density of (beyond 1012 active functions per cm2) comparable to that of the human cerebral cortex, while operating at much higher speed (up to 1020 operations per second per cm2), at acceptable power consumption. Our group has suggested a family of distributed crosspoint networks ("CrossNets") that are natural for implementation in CMOL technology, and has shown that such networks may be trained to perform at least the effective pattern recognition in the Hopfield mode. Work on CrossNet training to perform more complex tasks in under way.
  • Keywords
    CMOS analogue integrated circuits; nanowires; neural net architecture; pattern recognition; power consumption; reviews; Hopfield mode; areal density; crossnet training; crossnets; crosspoint networks distribution; human cerebral cortex; hybrid CMOS circuits; information processing systems; molecular circuits; nanowire circuits; neuromorphic architectures; neuromorphic circuits; power consumption; CMOS logic circuits; CMOS technology; Fabrication; MOSFETs; Nanoscale devices; Neuromorphics; Self-assembly; Single electron devices; Single electron transistors; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on
  • Print_ISBN
    0-7803-7976-4
  • Type

    conf

  • DOI
    10.1109/NANO.2003.1231787
  • Filename
    1231787