摘要
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 tech-nology.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 architec-tures.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-archi-tecture 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.
作者
渠鹏
纪兴龙
陈嘉杰
庞猛
李宇晨
刘晓义
张悠慧
Peng Qu;Xing-Long Ji;Jia-Jie Chen;Meng Pang;Yu-Chen Li;Xiao-Yi Liu;You-Hui Zhang(Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;Department of Precision Instrument,Tsinghua University,Beijing 100084,China)
基金
This work was supported by the National Natural Science Foundation of China under Grant Nos.62250006,62072266,and 61836004
the National Natural Science Foundation of China Youth Fund under Grant No.62202254,Beijing National Research Center for Information Science and Technology under Grant No.BNR2022RC01003
the Tsinghua University Initiative Scientific Research Program
the Suzhou-Tsinghua Innovation Leadership Program.