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Neuro-inspired System with Crossbar Array of Amorphous Metal-Oxide-Semiconductor Thin-Film Devices as Self-plastic Synapse Units

Letter Recognition of Five Alphabets

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Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11954))

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Abstract

Artificial intelligences are essential concept and indispensable in future smart societies, while neural networks are typical representative schemes that imitate human brains and mimic biological functions. However, the conventional neural networks are composed of lengthy software that is executed by high-spec computing hardware, the computer size is enormous, and power dissipation is huge. On the other hand, neuro-inspired systems are practical solutions consisting only of customized hardware, and the hardware size and power dissipation can be saved. Therefore, we have been studying neuro-inspired systems with amorphous metal-oxide-semiconductor (AOS) thin-film devices as synapse units and suggesting revised Hebbian learning that is automatically and locally conducted without additional control circuits. Here, the conductance degradation can be employed as self-plastic weight of synapse units. As a result, it is promising that the neuro-inspired systems become three-dimensional integrated systems, the hardware size can be very compact, the power dissipation can be very low, and all functions of biological brains are obtained. In this study, we have been developing neuro-inspired systems with crossbar array of AOS thin-film devices as self-plastic synapse units. First, the crossbar array is produced, and it is discovered that the electric current continuously decreases along the application time. Next, the neuro-inspired system is really constructed by a field-programmable-gate-array LSI and crossbar array, and it is validated that a function of letter recognition is acquired after learning operation. In particular, we succeed in the letter recognition of five alphabets in this paper, whereas we succeeded in that of three alphabets in the previous paper, which is theoretically discussed, namely, the theoretical maximum performance seems to be achieved. Once the fundamental operations are validated, further progressed functions will be achieved by greatening the device and circuit scales.

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Acknowledgments

This work is partially supported by KAKENHI (C) 16K06733, KAKENHI (C) 19K11876, Yazaki Memorial Foundation for Science and Technology, Support Center for Advanced Telecommunications Technology Research, Research Grants in the Natural Sciences from the Mitsubishi Foundation, Telecommunications Advancement Foundation, collaborative research with ROHM Semiconductor, collaborative research with KOA Corporation, Laboratory for Materials and Structures in Tokyo Institute of Technology, and Research Institute of Electrical Communication in Tohoku University.

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Authors and Affiliations

  1. Department of Electronics and Informatics, Ryukoku University, Seta, Otsu, 520-2194, Japan

    Mutsumi Kimura, Keisuke Ikushima, Toshimasa Hori & Ryo Tanaka

  2. Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama, Ikoma, 630-0192, Japan

    Mutsumi Kimura & Yasuhiko Nakashima

  3. High-Tech Research Center, Innovative Materials and Processing Research Center, Seta, Otsu, 520-2194, Japan

    Mutsumi Kimura & Tokiyoshi Matsuda

  4. Graduate School of Materials Science, Nara Institute of Science and Technology, Takayama, Ikoma, 630-0192, Japan

    Kenta Umeda

  5. Graduate School of Information Science, Nara Institute of Science and Technology, Takayama, Ikoma, 630-0192, Japan

    Tomoya Kameda

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  1. Mutsumi Kimura
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  2. Kenta Umeda
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  8. Yasuhiko Nakashima
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Correspondence to Mutsumi Kimura .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

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

    Minho Lee

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Kimura, M. et al. (2019). Neuro-inspired System with Crossbar Array of Amorphous Metal-Oxide-Semiconductor Thin-Film Devices as Self-plastic Synapse Units. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_40

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  • DOI: https://doi.org/10.1007/978-3-030-36711-4_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36710-7

  • Online ISBN: 978-3-030-36711-4

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