Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

• 作者: Xumeng Zhang Jian Lu Zhongrui Wang Rui Wang Jinsong Wei Tuo Shi Chunmeng Dou Zuheng Wu Jiaxue Zhu Dashan Shang Guozhong Xing Mansun Chan Qi Liu Ming Liu
• 来源期刊: 科学通报（英文版）
• 年，卷（期）: 2021,（16）
• 所属期刊栏目: ARTICLES
• 页码范围: 1624-1633
• 页数: 10页
• 语种: 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 net-works 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 memris-tive 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 implemen-tation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.