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Towards artificial general intelligence with hybrid Tianjic chip architecture

Pei, Jing ; Deng, Lei ; Song, Sen ; Zhao, Mingguo ; Zhang, Youhui ; Wu, Shuang ; Wang, Guanrui ; Zou, Zhe ; Wu, Zhenzhi ; He, Wei ; Chen, Feng ; Deng, Ning ; Wu, Si ; Wang, Yu ; Wu, Yujie ; Yang, Zheyu ; Ma, Cheng ; Li, Guoqi ; Han, Wentao ; Li, Huanglong ; Wu, Huaqiang ; Zhao, Rong ; Xie, Yuan ; Shi, Luping

Nature (London), 2019-08, Vol.572 (7767), p.106-111 [Periódico revisado por pares]

England: Nature Publishing Group

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Título:
Towards artificial general intelligence with hybrid Tianjic chip architecture
Autor: Pei, Jing ; Deng, Lei ; Song, Sen ; Zhao, Mingguo ; Zhang, Youhui ; Wu, Shuang ; Wang, Guanrui ; Zou, Zhe ; Wu, Zhenzhi ; He, Wei ; Chen, Feng ; Deng, Ning ; Wu, Si ; Wang, Yu ; Wu, Yujie ; Yang, Zheyu ; Ma, Cheng ; Li, Guoqi ; Han, Wentao ; Li, Huanglong ; Wu, Huaqiang ; Zhao, Rong ; Xie, Yuan ; Shi, Luping
Assuntos: Algorithms ; Artificial intelligence ; Artificial neural networks ; Bicycles ; Brain ; Coding ; Computational neuroscience ; Computer architecture ; Design and construction ; Equipment and supplies ; Field programmable gate arrays ; Formulations ; Hybrid systems ; Integrated circuits ; Intelligence ; Learning algorithms ; Machine learning ; Memory ; Nervous system ; Network topologies ; Neural coding ; Neural networks ; Neurons ; Neurosciences ; Object recognition ; Obstacle avoidance ; Platforms ; Science ; Semiconductor chips ; Technology application ; Tracking control ; Voice control ; Voice recognition
É parte de: Nature (London), 2019-08, Vol.572 (7767), p.106-111
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: There are two general approaches to developing artificial general intelligence (AGI) : 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 platforms , 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

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Towards artificial general intelligence with hybrid Tianjic chip architecture

England: Nature Publishing Group

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