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Time-Domain Weighted-Sum Calculation for Ultimately Low Power VLSI Neural Networks

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

Time-domain weighted-sum operation based on a spiking neuron model is discussed and evaluated from a VLSI implementation point of view. This calculation model is useful for extremely low-power operation because transition states in resistance and capacitance (RC) circuits can be used. Weighted summation is achieved with energy dissipation on the order of 1 fJ using the current CMOS VLSI technology if 1 G\(\varOmega \) order resistance can be used, where the number of inputs can be more than a hundred. This amount of energy is several orders of magnitude lower than that in conventional digital processors. In this paper, we show the software simulation results that verify the proposed calculation method for a 500-input neuron in a three-layer perceptron for digit character recognition.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Nos. 22240022 and 15H01706. Part of the work was carried out under the Collaborative Research Project of the Institute of Fluid Science, Tohoku University.

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Authors and Affiliations

  1. Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4, Hibikino, Wakamatsu-ku, Kitakyushu, 808-0196, Japan

    Quan Wang, Hakaru Tamukoh & Takashi Morie

Authors
  1. Quan Wang
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  2. Hakaru Tamukoh
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  3. Takashi Morie
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Corresponding author

Correspondence to Takashi Morie .

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Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Wang, Q., Tamukoh, H., Morie, T. (2016). Time-Domain Weighted-Sum Calculation for Ultimately Low Power VLSI Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

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