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A system hierarchy for brain-inspired computing

Author

Listed:
  • Youhui Zhang

    (Tsinghua University
    Tsinghua University
    Beijing National Research Center for Information Science and Technology)

  • Peng Qu

    (Tsinghua University
    Tsinghua University
    Beijing National Research Center for Information Science and Technology)

  • Yu Ji

    (Tsinghua University
    Tsinghua University
    Beijing National Research Center for Information Science and Technology)

  • Weihao Zhang

    (Tsinghua University
    Tsinghua University)

  • Guangrong Gao

    (University of Delaware)

  • Guanrui Wang

    (Tsinghua University
    Tsinghua University)

  • Sen Song

    (Tsinghua University
    Tsinghua University)

  • Guoqi Li

    (Tsinghua University
    Tsinghua University)

  • Wenguang Chen

    (Tsinghua University
    Beijing National Research Center for Information Science and Technology)

  • Weimin Zheng

    (Tsinghua University
    Beijing National Research Center for Information Science and Technology)

  • Feng Chen

    (Tsinghua University
    Tsinghua University)

  • Jing Pei

    (Tsinghua University
    Tsinghua University)

  • Rong Zhao

    (Tsinghua University)

  • Mingguo Zhao

    (Tsinghua University
    Tsinghua University)

  • Luping Shi

    (Tsinghua University
    Tsinghua University)

Abstract

Neuromorphic computing draws inspiration from the brain to provide computing technology and architecture with the potential to drive the next wave of computer engineering1–13. Such brain-inspired computing also provides a promising platform for the development of artificial general intelligence14,15. However, unlike conventional computing systems, which have a well established computer hierarchy built around the concept of Turing completeness and the von Neumann architecture16–18, there is currently no generalized system hierarchy or understanding of completeness for brain-inspired computing. This affects the compatibility between software and hardware, impairing the programming flexibility and development productivity of brain-inspired computing. Here we propose ‘neuromorphic completeness’, which relaxes the requirement for hardware completeness, and a corresponding system hierarchy, which consists of a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture. Using this hierarchy, various programs can be described as uniform representations and transformed into the equivalent executable on any neuromorphic complete hardware—that is, it ensures programming-language portability, hardware completeness and compilation feasibility. We implement toolchain software to support the execution of different types of program on various typical hardware platforms, demonstrating the advantage of our system hierarchy, including a new system-design dimension introduced by the neuromorphic completeness. We expect that our study will enable efficient and compatible progress in all aspects of brain-inspired computing systems, facilitating the development of various applications, including artificial general intelligence.

Suggested Citation

  • Youhui Zhang & Peng Qu & Yu Ji & Weihao Zhang & Guangrong Gao & Guanrui Wang & Sen Song & Guoqi Li & Wenguang Chen & Weimin Zheng & Feng Chen & Jing Pei & Rong Zhao & Mingguo Zhao & Luping Shi, 2020. "A system hierarchy for brain-inspired computing," Nature, Nature, vol. 586(7829), pages 378-384, October.
  • Handle: RePEc:nat:nature:v:586:y:2020:i:7829:d:10.1038_s41586-020-2782-y
    DOI: 10.1038/s41586-020-2782-y
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    Citations

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    Cited by:

    1. Ziqi Gao & Chenran Jiang & Jiawen Zhang & Xiaosen Jiang & Lanqing Li & Peilin Zhao & Huanming Yang & Yong Huang & Jia Li, 2023. "Hierarchical graph learning for protein–protein interaction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Herbert Jaeger & Beatriz Noheda & Wilfred G. Wiel, 2023. "Toward a formal theory for computing machines made out of whatever physics offers," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Helin Yang & Kwok-Yan Lam & Liang Xiao & Zehui Xiong & Hao Hu & Dusit Niyato & H. Vincent Poor, 2022. "Lead federated neuromorphic learning for wireless edge artificial intelligence," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Jens E. Pedersen & Steven Abreu & Matthias Jobst & Gregor Lenz & Vittorio Fra & Felix Christian Bauer & Dylan Richard Muir & Peng Zhou & Bernhard Vogginger & Kade Heckel & Gianvito Urgese & Sadasivan , 2024. "Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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