• DocumentCode
    3546226
  • Title

    Neurotransistor:≫MINUSa neuron-like high-functionality transistor implementing intelligence on silicon

  • Author

    Shibata, Tadashi ; Ohmi, Tadahiro

  • Author_Institution
    Dept. of Electron. Eng., Tohoku Univ., Sendai, Japan
  • fYear
    1995
  • fDate
    16-18 Oct 1995
  • Firstpage
    28
  • Lastpage
    37
  • Abstract
    The main theme of this paper is not a so-called neural network but a new-architecture intelligent electronic circuit implemented using the neuron-like high-functionality transistor as a basic circuit element. The transistor is a multiple-input-gate thresholding device and is called neuron MOSFET (neuMOS or v MOS for short) due to its functional similarity to a brain cell neuron. The vMOS circuits are characterized by a high degree of parallelism in hardware computation, a large flexibility in hardware configuration and a dramatic reduction in the circuit complexity as compared to conventional integrated circuits. As a result, a number of new concept circuits have been developed. As examples, a real-time reconfigurable logic circuit called flexware and an associative memory conducting fully-parallel search for the most similar are presented. The enhancement in the functionality at a very elemental transistor level is critically important in building human-like intelligent systems on silicon
  • Keywords
    MOSFET; neural chips; Si; associative memory; flexware; fully-parallel search; integrated circuit; intelligent electronic circuit; most similar; multiple-input-gate thresholding device; neuron MOSFET; neurotransistor; real-time reconfigurable logic circuit; silicon; Biological neural networks; Brain cells; Concurrent computing; Electronic circuits; Flexible printed circuits; Hardware; Intelligent networks; MOSFET circuits; Neurons; Parallel processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    VLSI Signal Processing, VIII, 1995. IEEE Signal Processing Society [Workshop on]
  • Conference_Location
    Sakai
  • Print_ISBN
    0-7803-2612-1
  • Type

    conf

  • DOI
    10.1109/VLSISP.1995.527474
  • Filename
    527474