One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units  

作　　者：李亚霖 时凯璐 朱一新 方晓 崔航源 万青 万昌锦 Yalin Li;Kailu Shi;Yixin Zhu;Xiao Fang;Hangyuan Cui;Qing Wan;Changjin Wan(School of Electronic Science&Engineering,and Collaborative Innovation Center of Advanced Microstructures,Nanjing University,Nanjing 210023,China;Yongjiang Laboratory(Y-LAB),Ningbo 315202,China)

机构地区：[1]School of Electronic Science&Engineering,and Collaborative Innovation Center of Advanced Microstructures,Nanjing University,Nanjing 210023,China [2]Yongjiang Laboratory(Y-LAB),Ningbo 315202,China

出　　处：《Chinese Physics B》2024年第6期569-573,共5页中国物理B（英文版）

基　　金：Project supported by the National Key Research and Development Program of China (Grant Nos. 2021YFA1202600 and 2023YFE0208600);in part by the National Natural Science Foundation of China (Grant Nos. 62174082, 92364106, 61921005, 92364204, and 62074075)。

摘　　要：Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

关 键 词：dropout neuronal unit synaptic transistors MEMRISTOR artificial neural network 

分 类 号：TN60[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]