神经形态工程学
计算机科学
可扩展性
尖峰神经网络
人工神经网络
计算机体系结构
记忆电阻器
人工智能
电子工程
工程类
数据库
作者
Yongbiao Zhai,Peng Xie,Jiahui Hu,Xue Chen,Zihao Feng,Ziyu Lv,Guanglong Ding,Kui Zhou,Ye Zhou,Su‐Ting Han
摘要
To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarization effect of 2D α-In2Se3, we designed a reconfigurable hardware platform, which can switch from continuously modulated conductance for emulating synapse to spiking behavior for mimicking neuron. More crucially, we demonstrate the application of such proof-of-concept reconfigurable 2D ferroelectric devices on a spiking neural network with an accuracy of 95.8% and self-adaptive grow-when required network with an accuracy of 85% by dynamically shrinking its nodes by 72%, which exhibits more powerful learning ability and efficiency than the static neural network.
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