Large-scale Brain-inspired Computing System BiCoSS: Its Architecture, Implementation and Application

被引：0
|
作者：
Yang S.-M. [1 ]
Hao X.-Y. [1 ]
Wang J. [1 ]
Li H.-Y. [2 ]
Wei X.-L. [1 ]
Yu H.-T. [1 ]
Deng B. [1 ]
机构：
[1] School of Electrical and Information Engineering, Tianjin University, Tianjin
[2] . School of Automation and Electrical Engineering, Tianjin University of Technology and Educations, Tianjin
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 09期
基金：
中国国家自然科学基金; 中国博士后科学基金; 天津市自然科学基金;
关键词：
Brain-like computing; Brain-like intelligence; Neuromorphic engineering; Spiking neural network;
D O I：
10.16383/j.aas.c190035
中图分类号：
学科分类号：
摘要：
Human brain has the ability of integrating multiple cognitive functions and strong autonomous learning capability. With the rapid development of neuroscience, it is important and necessary to implement a brain simulation platform with higher performance that is inspired by brain structure to further explore the brain intelligent and mechanism of cognitive behaviors. Inspired by the mechanism of human neural system, a multi-core brain simulation system BiCoSS is presented in this paper, which is based on neurocognitive structure. The presented system uses parallel computing field-programmable gate array (FPGA) as core processor, address event representation (AER) neural spikes as carrier of information transmission, neuron with cognitive computing functions as information processing unit. It realizes the real-time computing of cognitive behaviors in a large-scale neural network with four million neurons, and bridges the gap from the cellular dynamics level to comprehend the human brain cognition functions. The superior performance of BiCoSS is shown in terms of computing power, computing efficiency, power consumption, communication efficiency and scalability. BiCoSS realizes brain-like intelligence based on the computing architecture of brain information processing that is closer to the essence of neuroscience, and provides new effective methods for the research and application of neural cognition and brain-like computing. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2154 / 2169
页数：15
相关论文
共 33 条
  • [1] Poo Mu-Ming, Xu Bo, Tan Tie-Niu, Brain science and brainInspired intelligence technology-an overview, Bulletin of Chinese Academy of Sciences, 31, 7, pp. 725-736, (2016)
  • [2] Xu Bo, Liu Cheng-Lin, Zeng Yi, Research status and developments of brain-inspired intelligence, Bulletin of Chinese Academy of Sciences, 31, 7, pp. 793-802, (2016)
  • [3] Wang Li-Wei, Xu Li, Xu Ping, Yu Han-Chao, Kong Ming-Hui, Shen Yi, Zhang Yong-Qing, Brain science and brain-like intelligence research in Chinese Academy of Sciences, Bulletin of Chinese Academy of Sciences, 31, 7, pp. 747-754, (2016)
  • [4] Park H J, Friston K, Structural and functional brain networks: from connections to cognition, Science, 342, 6158, (2013)
  • [5] Merolla P A, Arthur J V, Alvarez-Icaza R, Cassidy A S, Sawada J, Akopyan F, Et al., A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, 345, 6197, pp. 668-673, (2014)
  • [6] Zenke F, Bohte S M, Clopath C, Comsa I M, Goltz J, Maass W, Et al., Visualizing a joint future of neuroscience and neuromorphic engineering, Neuron, 109, 4, pp. 571-575, (2021)
  • [7] Yang S M, Wei X L, Wang J, Deng B, Liu C, Yu H T, Li H Y, Efficient hardware implementation of the subthalamic nucleus-external globus pallidus oscillation system and its dynamics investigation, Neural Networks, 94, pp. 220-238, (2018)
  • [8] Yang S M, Deng B, Li H Y, Liu C, Wang J, Yu H T, Qin Y M, FPGA implementation of hippocampal spiking network and its real-time simulation on dynamical neuromodulation of oscillations, Neurocomputing, 282, pp. 262-276, (2018)
  • [9] Wang Xiao-Feng, Yang Ya-Dong, Research on structure model of general intelligent system based on ecological evolution, Acta Automatica Sinica, 46, 5, pp. 1017-1030, (2020)
  • [10] Neckar A, Fok S, Benjamin B V, Stewart T C, Oza N N, Voelker A R, Et al., Braindrop: A mixedsignal neuromorphic architecture with a dynamical systemsbased programming model, Proceedings of the IEEE, 107, 1, pp. 144-164, (2019)
1234