Towards “General Purpose” Brain-Inspired Computing System

被引:0
|
作者
Youhui Zhang [1 ]
Peng Qu [1 ]
Weimin Zheng [1 ]
机构
[1] the Department of Computer Science and Technology,Tsinghua University
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP38 [其他计算机];
学科分类号
081201 ;
摘要
Brain-inspired computing refers to computational models, methods, and systems, that are mainly inspired by the processing mode or structure of brain. A recent study proposed the concept of "neuromorphic completeness"and the corresponding system hierarchy, which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each other. As a position paper, this article analyzes the existing brain-inspired chips design characteristics and the current so-called "general purpose" application development frameworks for brain-inspired computing, as well as introduces the background and the potential of this proposal. Further, some key features of this concept are presented through the comparison with the Turing completeness and approximate computation, and the analyses of the relationship with "general-purpose" brain-inspired computing systems(it means that computing systems can support all computable applications). In the end, a promising technical approach to realize such computing systems is introduced, as well as the on-going research and the work foundation. We believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding chips. On this basis, it is expected to gradually realize "general purpose" brain-inspired computing system, in order to take into account the functionality completeness and application efficiency.
引用
收藏
页码:664 / 673
页数:10
相关论文
共 50 条
  • [1] Towards "General Purpose" Brain-Inspired Computing System
    Zhang, Youhui
    Qu, Peng
    Zheng, Weimin
    TSINGHUA SCIENCE AND TECHNOLOGY, 2021, 26 (05) : 664 - 673
  • [2] The development of general-purpose brain-inspired computing
    Zhang, Weihao
    Ma, Songchen
    Ji, Xinglong
    Liu, Xue
    Cong, Yuqing
    Shi, Luping
    NATURE ELECTRONICS, 2024, 7 (11): : 954 - 965
  • [3] Research on General-Purpose Brain-Inspired Computing Systems
    Qu, Peng
    Ji, Xing-Long
    Chen, Jia-Jie
    Pang, Meng
    Li, Yu-Chen
    Liu, Xiao-Yi
    Zhang, You-Hui
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2024, 39 (01) : 4 - 21
  • [4] TOWARDS BRAIN-INSPIRED COMPUTING
    Gingl, Zoltan
    Kish, Laszlo B.
    Khatri, Sunil P.
    FLUCTUATION AND NOISE LETTERS, 2010, 9 (04): : 403 - 412
  • [5] Technical Perspective: Research on General-Purpose Brain-Inspired Computing Systems
    Rhodes, Oliver
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2024, 39 (01) : 1 - 3
  • [6] A Hybrid Brain-Inspired Computing Architecture towards Artificial General Intelligence
    Shi, Luping
    PROCEEDINGS OF 2020 IEEE 19TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC 2020), 2020, : 2 - 2
  • [7] 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
    Luping Shi
    Nature, 2020, 586 : 378 - 384
  • [8] A system hierarchy for brain-inspired computing
    Zhang, Youhui
    Qu, Peng
    Ji, Yu
    Zhang, Weihao
    Gao, Guangrong
    Wang, Guanrui
    Song, Sen
    Li, Guoqi
    Chen, Wenguang
    Zheng, Weimin
    Chen, Feng
    Pei, Jing
    Zhao, Rong
    Zhao, Mingguo
    Shi, Luping
    NATURE, 2020, 586 (7829) : 378 - +
  • [9] Towards Brain-Inspired System Architectures
    Sterling, Thomas
    Brodowicz, Maciej
    Gilmanov, Timur
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8603 : 159 - 170
  • [10] Towards Brain-Inspired System Architectures
    Sterling, Thomas
    Brodowicz, Maciej
    Gilmanov, Timur
    BRAIN-INSPIRED COMPUTING, 2014, 8603 : 159 - 170