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Towards artificial general intelligence with hybrid Tianjic chip architecture

Author

Listed:
  • Jing Pei

    (Tsinghua University
    Tsinghua University)

  • Lei Deng

    (Tsinghua University)

  • Sen Song

    (Tsinghua University
    Tsinghua University)

  • Mingguo Zhao

    (Tsinghua University)

  • Youhui Zhang

    (Tsinghua University)

  • Shuang Wu

    (Tsinghua University
    Tsinghua University)

  • Guanrui Wang

    (Tsinghua University
    Tsinghua University)

  • Zhe Zou

    (Tsinghua University
    Tsinghua University)

  • Zhenzhi Wu

    (Lynxi Technologies)

  • Wei He

    (Tsinghua University
    Tsinghua University)

  • Feng Chen

    (Tsinghua University)

  • Ning Deng

    (CBICR, Tsinghua University)

  • Si Wu

    (Beijing Normal University)

  • Yu Wang

    (Tsinghua University)

  • Yujie Wu

    (Tsinghua University
    Tsinghua University)

  • Zheyu Yang

    (Tsinghua University
    Tsinghua University)

  • Cheng Ma

    (Tsinghua University
    Tsinghua University)

  • Guoqi Li

    (Tsinghua University
    Tsinghua University)

  • Wentao Han

    (Tsinghua University)

  • Huanglong Li

    (Tsinghua University
    Tsinghua University)

  • Huaqiang Wu

    (CBICR, Tsinghua University)

  • Rong Zhao

    (Singapore University of Technology and Design)

  • Yuan Xie

    (University of California Santa Barbara)

  • Luping Shi

    (Tsinghua University
    Tsinghua University)

Abstract

There are two general approaches to developing artificial general intelligence (AGI)1: computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2–8, retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as neuroscience-inspired models and algorithms is highly desirable. Here we present the Tianjic chip, which integrates the two approaches to provide a hybrid, synergistic platform. The Tianjic chip adopts a many-core architecture, reconfigurable building blocks and a streamlined dataflow with hybrid coding schemes, and can not only accommodate computer-science-based machine-learning algorithms, but also easily implement brain-inspired circuits and several coding schemes. Using just one chip, we demonstrate the simultaneous processing of versatile algorithms and models in an unmanned bicycle system, realizing real-time object detection, tracking, voice control, obstacle avoidance and balance control. Our study is expected to stimulate AGI development by paving the way to more generalized hardware platforms.

Suggested Citation

  • Jing Pei & Lei Deng & Sen Song & Mingguo Zhao & Youhui Zhang & Shuang Wu & Guanrui Wang & Zhe Zou & Zhenzhi Wu & Wei He & Feng Chen & Ning Deng & Si Wu & Yu Wang & Yujie Wu & Zheyu Yang & Cheng Ma & G, 2019. "Towards artificial general intelligence with hybrid Tianjic chip architecture," Nature, Nature, vol. 572(7767), pages 106-111, August.
    • Handle: RePEc:nat:nature:v:572:y:2019:i:7767:d:10.1038_s41586-019-1424-8
    DOI: 10.1038/s41586-019-1424-8
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    Cited by:

    1. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Zhang, Xu & Min, Fuhong & Dou, Yiping & Xu, Yeyin, 2023. "Bifurcation analysis of a modified FitzHugh-Nagumo neuron with electric field," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    3. Christoph Stöckl & Yukun Yang & Wolfgang Maass, 2024. "Local prediction-learning in high-dimensional spaces enables neural networks to plan," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Wang, Huan & Li, Yan-Fu, 2023. "Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Chenhao Wang & Xinyi Xu & Xiaodong Pi & Mark D. Butala & Wen Huang & Lei Yin & Wenbing Peng & Munir Ali & Srikrishna Chanakya Bodepudi & Xvsheng Qiao & Yang Xu & Wei Sun & Deren Yang, 2022. "Neuromorphic device based on silicon nanosheets," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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