TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity

doi:10.1109/TBCAS.2022.3189240

	TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity
	Wang, Haibing 1; He, Zhen 2; Wang, Tengxiao 1; He, Junxian 1; Zhou, Xichuan 1; Wang, Ying 3; Liu, Liyuan 4; Wu, Nanjian 4; Tian, Min 1; Shi, Cong 1
	2022-08-01
发表期刊	IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
ISSN	1932-4545
卷号	16 期号:4 页码:636-650
摘要	Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic hardware implementation. We propose the TripleBrain hardware core that tightly combines three common brain-inspired factors: the spike-based processing and plasticity, the self-organizing map (SOM) mechanism and the reinforcement learning scheme, to improve object recognition accuracy and processing throughput, while keeping low resource costs. The proposed hardware core is fully event-driven to mitigate unnecessary operations, and enables various on-chip learning rules (including the proposed SOM-STDP & R-STDP rule and the R-SOM-STDP rule regarded as the two variants of our TripleBrain learning rule) with different accuracy-latency tradeoffs to satisfy user requirements. An FPGA prototype of the neuromorphic core was implemented and elaborately tested. It realized high-speed learning (1349 frame/s) and inference (2698 frame/s), and obtained comparably high recognition accuracies of 95.10%, 80.89%, 100%, 94.94%, 82.32%, 100% and 97.93% on the MNIST, ETH-80, ORL-10, Yale-10, N-MNIST, Poker-DVS and Posture-DVS datasets, respectively, while only consuming 4146 (7.59%) slices, 32 (3.56%) DSPs and 131 (24.04%) Block RAMs on a Xilinx Zynq-7045 FPGA chip. Our neuromorphic core is very attractive for real-time resource-limited edge intelligent systems.
关键词	Neurons Neuromorphics Hardware System-on-chip Field programmable gate arrays Synapses Self-organizing feature maps Neuromorphic system spiking neural network spike-timing dependent plasticity self-organizing map reinforce- ment learning on-chip learning
DOI	10.1109/TBCAS.2022.3189240
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2019YFB2204303] ; National Natural Science Foundation of China[U20A20205] ; Key Project of Chongqing Science and Technology Foundation[cstc2019jcyj-zdxmX0017] ; Key Project of Chongqing Science and Technology Foundation[cstc2021ycjh-bgzxm0031] ; Chongqing Xianfeng Electronic Institute Co., Ltd through Pilot Research Project[H20201100] ; State Key Laboratory of Computer Architecture, ICT, CAS[CARCH201908] ; Chongqing Social Security Bureau and Human Resources Dept.[cx2020018]
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical ; Engineering, Electrical & Electronic
WOS记录号	WOS:000866527900017
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19779
专题	中国科学院计算技术研究所期刊论文
通讯作者	Shi, Cong
作者单位	1.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China 2.Tsinghua Univ, Sch Integrated Circuits, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Semicond, State Key Lab Superlattices & Microstruct, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Wang, Haibing,He, Zhen,Wang, Tengxiao,et al. TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity[J]. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS,2022,16(4):636-650.
APA	Wang, Haibing.,He, Zhen.,Wang, Tengxiao.,He, Junxian.,Zhou, Xichuan.,...&Shi, Cong.(2022).TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity.IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS,16(4),636-650.
MLA	Wang, Haibing,et al."TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity".IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 16.4(2022):636-650.