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                    大規模類腦計算系統BiCoSS: 架構、實現及應用

                    楊雙鳴 郝新宇 王江 李會艷 魏熙樂 于海濤 鄧斌

                    楊雙鳴, 郝新宇, 王江, 李會艷, 魏熙樂, 于海濤, 鄧斌. 大規模類腦計算系統BiCoSS: 架構、實現及應用. 自動化學報, 2021, 47(9): 1?16 doi: 10.16383/j.aas.c190035
                    引用本文: 楊雙鳴, 郝新宇, 王江, 李會艷, 魏熙樂, 于海濤, 鄧斌. 大規模類腦計算系統BiCoSS: 架構、實現及應用. 自動化學報, 2021, 47(9): 1?16 doi: 10.16383/j.aas.c190035
                    Yang Shuang-Ming, Hao Xin-Yu, Wang Jiang, Li Hui Yan, Wei Xi-Le, Yu Hai-Tao, Deng Bin. Large-scale brain-inspired computing system biCoSS: its architecture, implementation and application. Acta Automatica Sinica, 2021, 47(9): 1?16 doi: 10.16383/j.aas.c190035
                    Citation: Yang Shuang-Ming, Hao Xin-Yu, Wang Jiang, Li Hui Yan, Wei Xi-Le, Yu Hai-Tao, Deng Bin. Large-scale brain-inspired computing system biCoSS: its architecture, implementation and application. Acta Automatica Sinica, 2021, 47(9): 1?16 doi: 10.16383/j.aas.c190035

                    大規模類腦計算系統BiCoSS: 架構、實現及應用

                    doi: 10.16383/j.aas.c190035
                    基金項目: 國家自然科學基金(61871287, 61671320, 61601320, 61771330)62071324, 62006170, 天津市自然科學基金(18JCZDJC32000)資助,中國博士后科學基金(2020M680885),
                    詳細信息
                      作者簡介:

                      楊雙鳴:天津大學電氣自動化與信息工程學院博士研究生. 主要研究方向為類腦智能與神經計算. E-mail: yangshuangming@tju.edu.cn

                      郝新宇:天津大學電氣自動化與信息工程學院博士研究生. 主要研究方向為神經計算及FPGA實現. E-mail: haoxy@tju.edu.cn

                      王江:天津大學電氣自動化與信息工程學院教授. 主要研究方向為神經控制工程與神經科學. E-mail: jiangwang@tju.edu.cn

                      李會艷:天津職業技術師范大學自動化與電氣工程學院教授. 主要研究方向為非線性系統與神經網絡. E-mail: lhy2740@126.com

                      魏熙樂:天津大學電氣自動化與信息工程學院教授. 主要研究方向為神經控制工程與無創式腦調制技術. E-mail: xilewei@tju.edu.cn

                      于海濤:天津大學電氣自動化與信息工程學院副教授. 主要研究方向為神經系統建模與動力學分析. E-mail: htyu@tju.edu.cn

                      鄧斌:天津大學電氣自動化與信息工程學院教授. 主要研究方向為神經計算及其非線性動力學分析. E-mail: dengbin@tju.edu.cn

                    • 收稿日期 2019-01-14 錄用日期 2019-06-06 Manuscript?received?January?14,?2019;?accepted?June?6,?2019 國家自然科學基金(61871287, 61671320, 61601320, 61771330), 62071324, 62006170 天津市自然科學基金 (18JCZDJC32000) 資助中國博士后科學基金(2020M680885) Supported?by?National?Natural?Science?Foundation?of?China (61871287,?61671320,?61601320,?61771330) 62071324, 62006170, China Postdoctoral Science Foundation (Grant No. 2020M680885), Natural?Science Foundation?of?Tianjin,?China?(18JCZDJC32000) 本文責任編委?曾志剛 Recommended?by?Associate?Editor?ZENG?Zhi-Gang 1.?天津大學電氣自動化與信息工程學院?天津?300072 2.?天津職業技術師范大學自動化與電氣工程學院?天津?300222 1.?School?of?Electrical?and?Information?Engineering,?TianjinUniversity,?Tianjin?300072 2.?School?of?Automation?and?Elec-
                    • trical?Engineering,?Tianjin?University?of?Technology?and?Educations,?Tianjin?300222

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

                    Funds: Supported by National Natural Science Foundation of China (61871287, 61671320, 61601320, 61771330,62071324,62006170), Cina Postdoctoral Science Foundation (Grant No. 2020M680885), Natural Science Foundation of Tianjin, China (18JCZDJC32000)
                    More Information
                      Author Bio:

                      YANG Shuang-Ming Ph.D. candidate at the School of Electrical and Information Engineering, Tianjin University. His research interest covers brain-inspired intelligence and neural computing

                      HAO Xin-Yu Ph.D. candidate at the School of Electrical and Information Engineering, Tianjin University. His research interest covers neural computing and its FPGA implementation

                      WANG Jiang Professor at the School of Electrical and Information Engineering, Tianjin University. His research interest covers neural control engineering and neuroscience

                      LI Hui-Yan Professor at the School of Automation and Electrical Engineering, Tianjin University of Technology and Education. Her research interest covers nonlinear systems and neural networks

                      WEI Xi-Le Professor at the School of Electrical and Information Engineering, Tianjin University. His research interest covers neural control engineering and noninvasive brain modulation technology

                      YU Hai-Tao Associate professor at the School of Electrical and Information Engineering, Tianjin University. His research interest covers neural system modeling and dynamics analysis

                      DENG Bin Professor at the School of Electrical and Information Engineering, Tianjin University. His research interest covers neural computing and nonlinear dynamics analysis

                    • 摘要: 人腦具有協同多種認知功能的能力與極強的自主學習能力, 隨著腦與神經科學的快速發展, 亟需計算結構模擬人腦的、性能更強大的計算平臺進行人腦智能與認知行為機制的進一步探索. 受人腦神經機制的啟發, 本文提出了基于神經認知計算架構的眾核類腦計算系統BiCoSS, 該系統以并行計算的現場可編程門陣列(Field-programmable gate array, FPGA)為核心處理器, 以地址事件表達的神經放電作為信息傳遞載體, 以具有認知計算功能的神經元作為信息處理單元, 實現了四百萬神經元數量級大規模神經元網絡認知行為的實時計算, 填補了從細胞動力學層面理解人腦認知功能的鴻溝. 實驗結果從計算能力、計算效率、功耗、通信效率、可擴展性等方面顯示了BiCoSS系統的優越性能. BiCoSS通過人腦信息處理的計算架構以更貼近神經科學本質的模式實現了類腦智能; 同時, BiCoSS為神經認知和類腦計算的研究和應用提供了新的有效手段.
                      1)  收稿日期 2019-01-14 錄用日期 2019-06-06 Manuscript?received?January?14,?2019;?accepted?June?6,?2019 國家自然科學基金(61871287, 61671320, 61601320, 61771330), 62071324, 62006170 天津市自然科學基金 (18JCZDJC32000) 資助中國博士后科學基金(2020M680885) Supported?by?National?Natural?Science?Foundation?of?China (61871287,?61671320,?61601320,?61771330) 62071324, 62006170, China Postdoctoral Science Foundation (Grant No. 2020M680885), Natural?Science Foundation?of?Tianjin,?China?(18JCZDJC32000) 本文責任編委?曾志剛 Recommended?by?Associate?Editor?ZENG?Zhi-Gang 1.?天津大學電氣自動化與信息工程學院?天津?300072 2.?天津職業技術師范大學自動化與電氣工程學院?天津?300222 1.?School?of?Electrical?and?Information?Engineering,?TianjinUniversity,?Tianjin?300072 2.?School?of?Automation?and?Elec-
                      2)  trical?Engineering,?Tianjin?University?of?Technology?and?Educations,?Tianjin?300222
                    • 圖  1  BiCoSS系統架構

                      Fig.  1  System architecture of BiCoSS

                      圖  2  BiCoSS系統實物圖

                      Fig.  2  Physical map of BiCoSS

                      圖  3  BiCoSS系統神經元網絡計算架構

                      Fig.  3  Neural network computing architecture of BiCoSS system

                      圖  4  BiCoSS系統神經元與突觸計算模塊架構

                      Fig.  4  Neuron and synapse computing architecture of BiCoSS system

                      圖  5  生物啟發的神經元網絡放電行為與突觸可塑性

                      Fig.  5  Spiking activities of biologically inspired neuron model and STDP characteristics

                      圖  6  BiCoSS系統的神經放電信息路由

                      Fig.  6  Spike information routing of BiCoSS system

                      圖  7  模型實現的性能分析

                      Fig.  7  Analysis performance of model implementation

                      圖  8  BiCoSS系統性能分析

                      Fig.  8  Performance analysis of BiCoSS system

                      圖  9  BiCoSS系統神經元網絡單元平均延遲

                      Fig.  9  Average latency of neural network unit on BiCoSS

                      圖  10  實驗系統實物圖與計算結果

                      Fig.  10  Experimental setup of BiCoSS system and computing results

                      圖  11  基于BiCoSS系統的認知計算

                      Fig.  11  Cognition computing based on BiCoSS system

                      表  1  當前路由器相關地址編碼

                      Table  1  The address coding of the current router

                      當前節點子節點鄰居節點父節點負責神經計算單元
                      001, 0000001, 0100010, 00000000; 0001, 0010; 0011
                      001, 0100001, 0100010, 00000000; 0001, 0010; 0011
                      001, 1000001, 0100010, 00000000; 0001, 0010; 0011
                      001, 1100001, 0100010, 00000000; 0001, 0010; 0011
                      010, 0000001, 0100010, 00000000; 0001, 0010; 0011
                      010, 1000001, 0100010, 00000000; 0001, 0010; 0011
                      下載: 導出CSV

                      表  2  與當前代表性大規模類腦計算系統比較

                      Table  2  The comparison with the state-of-the art large-scale brain-inspired computing systems

                      類腦計算系統實現模型學習規模擴展性
                      BrainScaleS[24]模擬AIFSTDP4 MN2
                      Truenorth[5]數字LIF1 MN2
                      Neurogrid[22]混合QIF1 M2$^{N}$
                      SpiNNaker[21]數字任意STDP1 BN2
                      LaCSNN[28]數字任意STDP1 MN2
                      BlueHive[31]數字任意64 kN2
                      IFAT[32]模擬LIF65 k2$^{N}$
                      HiAER[29]模擬LIF1 M2$^{N}$
                      BiCoSS 數字任意STDP4 M4$\cdot $2$^{N}$
                      下載: 導出CSV

                      表  3  基底核模型中不同細胞的參數值

                      Table  3  Parameter values of different cells in the basal ganglia model

                      參數GPeGPiSTN
                      $a$0.10.10.005
                      $b$0.20.20.265
                      $c$?65?65?65
                      $d$221.5
                      $I^{x}$(nA)101030
                      $E_{ {\rm{AMPA} } }$(mV)000
                      $E_{ {\rm{NMDA} } }$(mV)000
                      $E_{ {\rm{GABA} } }$(mV)?60?60?60
                      ${\tau }_{ {\rm{AMPA} } }$(ms)666
                      ${\tau }_{{\rm{NMDA}}}$(ms)160160160
                      ${\tau }_{{\rm{GABA}}}$(ms)444
                      $W_{{\rm{Str}}D2\to GPe}$0.8?
                      $W_{{\rm{Str}}D1\to GPi}$1
                      $W_{{\rm{STN}}\to GPi}$1.15
                      下載: 導出CSV

                      表  4  小腦模型中不同細胞的參數值

                      Table  4  Parameter values of different cerebellar cells

                      GRGOPCBSVNIO
                      $\theta $(mV)?35?52?55?55?38?50
                      $C$(pF)3.128106107122.310
                      $G_{{\rm{leak}}}$(nS)0.432.32.322.321.630.67
                      $E_{{\rm{leak}}}$(mV)?58?55?68?68?56?60
                      $G_{{\rm{exc1}}}$(nS)0.158436.411331
                      $G_{{\rm{exc2}}}$(nS)0.02163.00317
                      $G_{{\rm{exc3}}}$(nS)6.097
                      ${\tau }_{{\rm{exc1}}}$(ms)1.21.58.38.39.910
                      ${\tau }_{{\rm{exc2}}}$(ms)523130.6
                      ${\tau }_{{\rm{exc3}}}$(ms)170
                      $E_{{\rm{exc}}}$(mV)000000
                      $G_{{\rm{inh1}}}$(nS)0.0121300.18
                      $G_{{\rm{inh2}}}$(nS)0.016
                      ${\tau }_{{\rm{inh1}}}$(ms)71042.310
                      ${\tau }_{{\rm{inh2}}}$(ms)59
                      $E_{{\rm{inh}}}$(mV)?82?75?88?75
                      $G_{{\rm{ahp}}}$(nS)1201001501
                      $E_{{\rm{ahp}}}$(mV)?82?72.7?70?70?70?75
                      ${\tau }_{{\rm{ahp}}}$(ms)5552.52.510
                      $I$(nA)250700
                      下載: 導出CSV

                      表  5  皮層?基底核?丘腦皮層模型中不同神經元的參數值

                      Table  5  Parameter values of different cells in the cortico-basal ganglia-thalamocortical model

                      $a$$b$$c$$d$$I_{{\rm{app}}}$(pA)
                      GPe0.0050.585?65410
                      GPi0.0050.585?65410
                      STN0.0060.262?6525
                      TC0.0020.2?6520
                      下載: 導出CSV

                      表  6  皮層?基底核?丘腦皮層模型網絡連接權重

                      Table  6  Parameter values of synaptic coupling weight in the cortico-basal ganglia-thalamocortical model

                      源節點$\to $目的節點突觸連接權重$g_{ij}$
                      GPe$\to $GPe0.075 + $g_{{\rm{inc}}}$
                      GPe$\to $STN0.025 + $g_{{\rm{inc}}}$
                      GPe$\to $GPi0.015 + $g_{{\rm{inc}}}$
                      STN$\to $GPe0.075 + $g_{{\rm{inc}}}$
                      STN$\to $GPi0.01 + 5$g_{ {\rm{inc} } }$
                      GPi$\to $TC0.01 + 5$g_{ {\rm{inc} } }$
                      下載: 導出CSV
                      360彩票
                    • [1] 蒲慕明, 徐波, 譚鐵牛. 腦科學與類腦研究概述. 中國科學院院刊, 2016, 31(7): 725?736

                      Poo Mu-Ming, Xu Bo, Tan Tie-Niu. Brain science and brainInspired intelligence technology-an overview. Bulletin of Chinese Academy of Sciences, 2016, 31(7): 725?736
                      [2] 徐波, 劉成林, 曾毅. 類腦智能研究現狀與發展思考. 中國科學院院刊, 2016, 31(7): 793?802

                      Xu Bo, Liu Cheng-Lin, Zeng Yi. Research status and developments of brain-inspired intelligence. Bulletin of Chinese Academy of Sciences, 2016, 31(7): 793?802
                      [3] 王力為, 許麗, 徐萍, 于漢超, 孔明輝, 沈毅, 張永清. 面向未來的中國科學院腦科學與類腦智能研究—強化基礎研究, 推進深度融合. 中國科學院院刊, 2016, 31(7): 747?754

                      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, 2016, 31(7): 747?754
                      [4] Park H J, Friston K. Structural and functional brain networks: from connections to cognition. Science, 2013, 342(6158): 1238411 doi: 10.1126/science.1238411
                      [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, 2014, 345(6197): 668?673 doi: 10.1126/science.1254642
                      [6] Izhikevich E M, Edelman G M. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences, 2008, 105(9): 3593?3598 doi: 10.1073/pnas.0712231105
                      [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, 2018, 94: 220?238
                      [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, 2018, 282: 262?276 doi: 10.1016/j.neucom.2017.12.031
                      [9] 王曉峰, 楊亞東. 基于生態演化的通用智能系統結構模型研究. 自動化學報, 2020, 46(5): 1017?1030 doi: 10.3969/j.issn.1003-8930.2019.01.001

                      Wang Xiao-Feng, Yang Ya-Dong. Research on structure model of general intelligent system based on ecological evolution. Acta Automatica Sinica, 2020, 46(5): 1017?1030 doi: 10.3969/j.issn.1003-8930.2019.01.001
                      [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, 2019, 107(1): 144?164 doi: 10.1109/JPROC.2018.2881432
                      [11] 張慧, 王坤峰, 王飛躍. 深度學習在目標視覺檢測中的應用進展與展望. 自動化學報, 2017, 43(8): 1289?1305

                      Zhang Hui, Wang Kun-Feng, Wang Fei-Yue. Advances and perspectives on applications of deep learning in visual object detection. Acta Automatica Sinica, 2017, 43(8): 1289?1305
                      [12] 陳偉宏, 安吉堯, 李仁發, 李萬里. 深度學習認知計算綜述. 自動化學報, 2017, 43(11): 1886?1997

                      Chen Wei-Hong, An Ji-Yao, Li Ren-Fa, Li Wan-Li. Review on deep-learning-based cognitive computing. Acta Automatica Sinica, 2017, 43(11): 1886?1997
                      [13] Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-inspired artificial intelligence. Neuron, 2017, 95(2): 245?258 doi: 10.1016/j.neuron.2017.06.011
                      [14] Thakur C S, Molin J L, Cauwenberghs G, Indiveri G, Kumar K, Qian N, et al. Large-scale neuromorphic spiking array processors: A quest to mimic the brain. Frontiers in Neuroscience, 2018, 12: 891 doi: 10.3389/fnins.2018.00891
                      [15] Eliasmith C, Trujillo O. The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 2014, 25: 1?6 doi: 10.1016/j.conb.2013.09.009
                      [16] Izhikevich E M, Edelman G M. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences, 2008, 105(9): 3593?3598 doi: 10.1073/pnas.0712231105
                      [17] Yang S M, Deng B, Wang J, Li H Y, Lu M L, Che Y Q, et al. Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(1): 148?162
                      [18] Markram H. The blue brain project. Nature Reviews Neuroscience, 2006, 7(2): 153 doi: 10.1038/nrn1848
                      [19] Markram H. The human brain project. Scientific American, 2012, 306(6): 50?55 doi: 10.1038/scientificamerican0612-50
                      [20] Insel T R, Landis S C, Collins F S. The NIH brain initiative. Science, 2013, 340(6133): 687?688 doi: 10.1126/science.1239276
                      [21] Furber S B, Galluppi F, Temple S, Plana L A. The SpiNNaker Project. Proceedings of the IEEE, 2014, 102(5): 652?665 doi: 10.1109/JPROC.2014.2304638
                      [22] Benjamin B V, Gao P, McQuinn E, Choudhary S, Chandrasekaram A R, Bussat J M. Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 2014, 102(5): 699?716 doi: 10.1109/JPROC.2014.2313565
                      [23] Kumar S. Introducing qualcomm zeroth processors: Brain-inspired computing. Qualcomm OnQ Blog, 2013: 1?11
                      [24] Petrovici M A, Vogginger B, Müller P,Breitwieser O, Lundqvist M, Muller L, et al. Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms. PloS one, 2014, 9(10): e108590 doi: 10.1371/journal.pone.0108590
                      [25] Chen T S, Du Z D, Sun N H, Wang J, Wu C Y, Chen Y J, Temam O. Diannao: A small-footprint high-throughput accelerator for ubiquitous machinelearning. ACM Sigplan Notices, 2014, 49(4): 269?284
                      [26] Chen Y J, Luo T, Liu S L, Zhang S J, He L Q, Wang J, et al. Dadiannao: A machinelearning supercomputer. In: Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture, Cambridge, UK: IEEE, 2014. 609?622
                      [27] Song S, Miller K D, Abbott L F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 2000, 3(9): 919?926 doi: 10.1038/78829
                      [28] Yang S M, Wang J, Deng B, Liu C, Li H Y, Fietkiewicz C, et al. Real-time neuromorphic system for large-scale conductance-based spiking neural networks. IEEE Transactions on Cybernetics, 2019, 49(7): 2490?2503
                      [29] Park J, Yu T, Joshi S, Maier C, Cauwenberghs G. Hierarchical address event routing for reconfigurable large-scale neuromorphic systems. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2408?2422 doi: 10.1109/TNNLS.2016.2572164
                      [30] Pinsky P F, Rinzel J. Synchrony measures for biological neural networks. Biological Cybernetics, 1995, 73(2): 129?137 doi: 10.1007/BF00204051
                      [31] Moore S W, Fox P J, Marsh S J T, Markettos A T, Mujumdar A. Bluehive—A field-programmable custom computing machine for extremescale real-time neural network simulation. In: Proceedings of the 20th IEEE International Symposium on Field-Programmable Custom Computing Machines. Toronto, ON, Canada: IEEE, 2012. 133−140
                      [32] Yu T, Park J, Joshi S, Maier C, Cauwenberghs G. 65k-neuron integrate-and-fire array transceiver with address-event reconfigurable synaptic routing. In: Proceedings of the 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), Hsinchu, China, 2012. 21?24
                      [33] Yamazaki T, Tanaka S. A spiking network model for passage-of-time representation in the cerebellum. European Journal of Neuroscience, 2007, 26(8): 2279?2292 doi: 10.1111/j.1460-9568.2007.05837.x
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                    出版歷程
                    • 收稿日期:  2019-01-14
                    • 錄用日期:  2019-06-06
                    • 網絡出版日期:  2021-07-26

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