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Research on General-Purpose Brain-Inspired Computing Systems

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Abstract

Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence (AGI), and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips, basic software and hardware, and algorithms/applications that embody this technology. While the system is developing rapidly, it faces various challenges and opportunities brought by interdisciplinary research, including the issue of software and hardware fragmentation. This paper analyzes the status quo of brain-inspired computing systems. Enlightened by some design principle and methodology of general-purpose computers, it is proposed to construct “general-purpose” brain-inspired computing systems. A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware, which can flexibly support various brain-inspired computing applications and neuromorphic chips with different architectures. Further, this paper introduces our recent work in these aspects, including the ANN (artificial neural network)/SNN (spiking neural network) development tools, the hardware agnostic compilation infrastructure, and the chip micro-architecture with high flexibility of programming and high performance; these studies show that the “general-purpose” system can remarkably improve the efficiency of application development and enhance the productivity of basic software, thereby being conductive to accelerating the advancement of various brain-inspired algorithms and applications. We believe that this is the key to the collaborative research and development, and the evolution of applications, basic software and chips in this field, and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Peng Qu, Jia-Jie Chen, Meng Pang, Yu-Chen Li, Xiao-Yi Liu & You-Hui Zhang

  2. Department of Precision Instrument, Tsinghua University, Beijing, 100084, China

    Xing-Long Ji

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Peng Qu, Xing-Long Ji, Jia-Jie Chen, Meng Pang, Yu-Chen Li and Xiao-Yi Liu are Co-First Author (Peng Qu wrote the methodological implications from the field of general purpose computing, Xing-Long Ji wrote the content about the framework for HNNs and the Tianjic chip, Jia-Jie Chen wrote the content of instruction set and microarchitecture, Meng Pang wrote the section of the framework for learning algorithms, Yu-Chen Li wrote the section of compilation infrastructure, and Xiao-Yi Liu wrote the current state of research in this field. The above several have made equal contributions to the paper.)

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Qu, P., Ji, XL., Chen, JJ. et al. Research on General-Purpose Brain-Inspired Computing Systems. J. Comput. Sci. Technol. 39, 4–21 (2024). https://doi.org/10.1007/s11390-023-4002-3

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