Towards artificial general intelligence with hybrid Tianjic chip architecture

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, Guoqi Li, Wentao Han, Huanglong Li, Huaqiang Wu, Rong Zhao, Yuan Xie and Luping Shi ()
Additional contact information
Jing Pei: Tsinghua University
Lei Deng: Tsinghua University
Sen Song: Tsinghua University
Mingguo Zhao: Tsinghua University
Youhui Zhang: Tsinghua University
Shuang Wu: Tsinghua University
Guanrui Wang: Tsinghua University
Zhe Zou: Tsinghua University
Zhenzhi Wu: Lynxi Technologies
Wei He: Tsinghua University
Feng Chen: Tsinghua University
Ning Deng: CBICR, Tsinghua University
Si Wu: Beijing Normal University
Yu Wang: Tsinghua University
Yujie Wu: Tsinghua University
Zheyu Yang: Tsinghua University
Cheng Ma: Tsinghua University
Guoqi Li: Tsinghua University
Wentao Han: Tsinghua University
Huanglong Li: 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

Nature, 2019, vol. 572, issue 7767, 106-111

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41586-019-1424-8

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