气候变化领域集成服务门户(CCPortal): Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

CCPortal

DOI	10.1016/j.scib.2021.04.014
	Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks
	Zhang X.; Lu J.; Wang Z.; Wang R.; Wei J.; Shi T.; Dou C.; Wu Z.; Zhu J.; Shang D.; Xing G.; Chan M.; Liu Q.; Liu M.
发表日期	2021
ISSN	20959273
起始页码	1624
结束页码	1633
卷号	66 期号:16
英文摘要	Spiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems. © 2021 Science China Press
关键词	Fully hardware Hybrid neuron In-situ learning Memristor Spiking neural network
英文关键词	CMOS integrated circuits; Data handling; Memristors; Neural networks; Fully hardware; Human brain; Hybrid neuron; In-situ learning; Memristor; Neural-networks; Neuromorphic computing; Neuron functions; Spiking neural network; Spiking Neurones; Neurons
语种	英语
来源期刊	Science Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/207674
作者单位	Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, Hong Kong; Zhejiang Laboratory, Hangzhou, 311122, China; Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, Hong Kong
推荐引用方式 GB/T 7714	Zhang X.,Lu J.,Wang Z.,et al. Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks[J],2021,66(16).
APA	Zhang X..,Lu J..,Wang Z..,Wang R..,Wei J..,...&Liu M..(2021).Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks.Science Bulletin,66(16).
MLA	Zhang X.,et al."Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks".Science Bulletin 66.16(2021).