• DocumentCode
    1082296
  • Title

    Real-Time Reconfigurable Subthreshold CMOS Perceptron

  • Author

    Aunet, Snorre ; Oelmann, Bengt ; Norseng, Per Andreas ; Berg, Yngvar

  • Author_Institution
    Univ. of Oslo, Oslo
  • Volume
    19
  • Issue
    4
  • fYear
    2008
  • fDate
    4/1/2008 12:00:00 AM
  • Firstpage
    645
  • Lastpage
    657
  • Abstract
    In this paper, a new, real-time reconfigurable perceptron circuit element is presented. A six-transistor version used as a threshold gate, having a fan-in of three, producing adequate outputs for threshold of T = 1,2 and 3 is demonstrated by chip measurements. Subthreshold operation for supply voltages in the range of 100-350 mV is shown. The circuit performs competitively with a standard static complimentary metal-oxide-semiconductor (CMOS) implementation when maximum speed and energy delay product are taken into account, when used in a ring oscillator. Functionality per transistor is, to our knowledge, the highest reported for a variety of comparable circuits not based on floating gate techniques. Statistical simulations predict probabilities for making working circuits under mismatch and process variations. The simulations, in 120-nm CMOS, also support discussions regarding lower limits to supply voltage and redundancy. A brief discussion on how the circuit may be exploited as a basic building block for future defect tolerant mixed signal circuits, as well as neural networks, exploiting redundancy, is included.
  • Keywords
    CMOS analogue integrated circuits; logic circuits; perceptrons; defect tolerance; floating gate techniques; perceptron circuit element; real time reconfigurable subthreshold CMOS perceptron; ring oscillator; static complimentary metal-oxide-semiconductor; threshold gate; Complimentary metal–oxide–semiconductor (CMOS); defect tolerant; nanoscale; perceptron; subthreshold; threshold element; Analog-Digital Conversion; Compulsive Behavior; Equipment Design; Equipment Failure Analysis; Humans; Neural Networks (Computer); Oxides; Semiconductors; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2007.912572
  • Filename
    4457800