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

doi:10.1088/1674-1056/ad39d6

Abstract 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 25pJ/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 to 40%. 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.

Keywords: dropout neuronal unit synaptic transistors memristor artificial neural network

Received: 08 March 2024
Revised: 19 March 2024
Accepted manuscript online: 03 April 2024

PACS:	84.35.+i	(Neural networks)
	73.40.Mr	(Semiconductor-electrolyte contacts)
	77.55.-g	(Dielectric thin films)
	81.05.Gc	(Amorphous semiconductors)

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

Corresponding Authors: Qing Wan, Changjin Wan
E-mail: wanqing@nju.edu.cn;cjwan@nju.edu.cn

Cite this article:

Yalin Li(李亚霖), Kailu Shi(时凯璐), Yixin Zhu(朱一新), Xiao Fang(方晓), Hangyuan Cui(崔航源), Qing Wan(万青), and Changjin Wan(万昌锦) One memristor-one electrolyte-gated transistor-based high energy-efficient dropout neuronal units 2024 Chin. Phys. B 33 068401

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

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