• DocumentCode
    727016
  • Title

    Hyperbolic tangent passive resistive-type neuron

  • Author

    Shamsi, Jafar ; Amirsoleimani, Amirali ; Mirzakuchaki, Sattar ; Ahmade, Arash ; Alirezaee, Shahpour ; Ahmadi, Majid

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran
  • fYear
    2015
  • fDate
    24-27 May 2015
  • Firstpage
    581
  • Lastpage
    584
  • Abstract
    In this paper, design of a passive resistive-type neuron is proposed to generate the hyperbolic tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal hyperbolic tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog hyperbolic tangent designed neuron.
  • Keywords
    CMOS integrated circuits; hyperbolic equations; low-power electronics; neural nets; pattern recognition; CMOS technology; activation function; hyperbolic tangent function; neuron circuit design; passive resistive-type neuron; pattern recognition neural network; power consumption; Artificial neural networks; Biological neural networks; CMOS integrated circuits; Neurons; Pattern recognition; Power demand; Resistors; Artificial Neural Networks; CMOS; Hyperbolic Tangent Activation Function; Neuron; Pattern Recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
  • Conference_Location
    Lisbon
  • Type

    conf

  • DOI
    10.1109/ISCAS.2015.7168700
  • Filename
    7168700