A system hierarchy for brain-inspired computing

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 and Luping Shi ()
Additional contact information
Youhui Zhang: Tsinghua University
Peng Qu: Tsinghua University
Yu Ji: Tsinghua University
Weihao Zhang: Tsinghua University
Guangrong Gao: University of Delaware
Guanrui Wang: Tsinghua University
Sen Song: Tsinghua University
Guoqi Li: Tsinghua University
Wenguang Chen: Tsinghua University
Weimin Zheng: Tsinghua University
Feng Chen: Tsinghua University
Jing Pei: Tsinghua University
Rong Zhao: Tsinghua University
Mingguo Zhao: Tsinghua University
Luping Shi: Tsinghua University

Nature, 2020, vol. 586, issue 7829, 378-384

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41586-020-2782-y

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