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Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization

Author

Listed:
  • Rui Wang

    (Institute of Microelectronics Chinese Academy of Sciences
    Fudan University
    University of Chinese Academy of Sciences)

  • Tuo Shi

    (Institute of Microelectronics Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Zhejiang Laboratory)

  • Xumeng Zhang

    (Fudan University)

  • Jinsong Wei

    (Institute of Microelectronics Chinese Academy of Sciences
    Zhejiang Laboratory)

  • Jian Lu

    (Institute of Microelectronics Chinese Academy of Sciences
    Zhejiang Laboratory)

  • Jiaxue Zhu

    (Institute of Microelectronics Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zuheng Wu

    (Institute of Microelectronics Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Qi Liu

    (Institute of Microelectronics Chinese Academy of Sciences
    Fudan University
    University of Chinese Academy of Sciences)

  • Ming Liu

    (Institute of Microelectronics Chinese Academy of Sciences
    Fudan University
    University of Chinese Academy of Sciences)

Abstract

A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaOx/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.

Suggested Citation

  • Rui Wang & Tuo Shi & Xumeng Zhang & Jinsong Wei & Jian Lu & Jiaxue Zhu & Zuheng Wu & Qi Liu & Ming Liu, 2022. "Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29411-4
    DOI: 10.1038/s41467-022-29411-4
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    References listed on IDEAS

    as
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    5. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
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    Cited by:

    1. Peng Chen & Fenghao Liu & Peng Lin & Peihong Li & Yu Xiao & Bihua Zhang & Gang Pan, 2023. "Open-loop analog programmable electrochemical memory array," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Thomas Dalgaty & Filippo Moro & Yiğit Demirağ & Alessio Pra & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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