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

[1] López C 2023 Advanced Materials 35 2208683
[2] Prezioso M, Merrikh-Bayat F, Hoskins B D, Adam G C, Likharev K K and Strukov D B 2015 Nature 521 61
[3] Ahn E C 2020 npj 2D Materials and Applications 4 17
[4] Sun K, Chen J and Yan X 2021 Adv. Funct. Mater. 31 2006773
[5] Zhu L Q, Wan C J, Guo L Q, Shi Y and Wan Q 2014 Nat. Commun. 5 3158
[6] Zhu Y, He Y, Chen C, Zhu L, Mao H, Zhu Y, Wang X, Yang Y, Wan C and Wan Q 2022 Appl. Phys. Lett. 120 113504
[7] Zhu Y, Peng B, Zhu L, Chen C, Wang X, Mao H, Zhu Y, Fu C, Ke S, Wan C and Wan Q 2022 Appl. Phys. Lett. 121 133502
[8] Lin X, Long H, Ke S, Wang Y, Zhu Y, Chen C, Wan C and Wan Q 2022 Chin. Phys. Lett. 39 068501
[9] Wu X, Zhang Y, Zhang T and Zhang B 2023 Chin. Phys. B 32 100506
[10] Xiang T, Chen F, Li X, Wang X, Yan Y and Wang L 2023 Chin. Phys. B 32 117301
[11] Yang Y, Fu C, Ke S, Cui H, Fang X, Wan C and Wan Q 2023 Chin. Phys. B 32 118101
[12] Amirsoleimani A, Alibart F, Yon V, Xu J, Pazhouhandeh M R, Ecoffey S, Beilliard Y, Genov R and Drouin D 2020 Advanced Intelligent Systems 2 2000115
[13] Demasius K U, Kirschen A and Parkin S 2021 Nat. Electron. 4 748
[14] Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R 2014 Journal of Machine Learning Research 15 1929
[15] Huang H M, Xiao Y, Yang R, Yu Y T, He H K, Wang Z and Guo X 2020 Adv. Sci. 7 2001842
[16] Wang Z, Joshi S, Savel’ev S E, Jiang H, Midya R, Lin P, Hu M, Ge N, Strachan J P, Li Z, Wu Q, Barnell M, Li G L, Xin H L, Williams R S, Xia Q and Yang J J 2017 Nat. Mater. 16 101
[17] Wang Z, Wu H, Burr G W, Hwang C S, Wang K L, Xia Q and Yang J J 2020 Nat. Rev. Mater. 5 173
[18] Wan C, Zhou J, Shi Y and Wan Q 2014 IEEE Electron Dev. Lett. 35 414
[19] Wan X, Feng P, Wu G D, Shi Y and Wan Q 2015 IEEE Electron Dev. Lett. 36 204
[20] Shao F, Feng P, Wan C, Wan X, Yang Y, Shi Y and Wan Q 2017 Adv. Electron. Mater. 3 1600509
[21] Huang Q, Huang R, Pan Y, Tan S and Wang Y 2014 IEEE Electron Dev. Lett. 35 877
[22] Mazady A and Anwar M 2014 IEEE Trans. Electron Dev. 61 1054
[23] Sanjar K, Rehman A, Paul A and JeongHong K 2020 8th International Conference on Orange Technology (ICOT), 18-21 December, 2020, pp. 1-4
[24] Zhu Y, Peng B, Zhu L, Chen C, Wang X, Mao H, Zhu Y, Fu C, Ke S, Wan C and Wan Q 2022 Appl. Phys. Lett. 121 133502
[25] Mendenhall J and Meiler J 2016 Journal of Computer-Aided Molecular Design 30 177

[1]	Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application Dawei Ding(丁大为), Yan Niu(牛炎), Hongwei Zhang(张红伟), Zongli Yang(杨宗立), Jin Wang(王金), Wei Wang(王威), and Mouyuan Wang(王谋媛). Chin. Phys. B, 2024, 33(5): 050503.
[2]	Dynamics and synchronization of neural models with memristive membranes under energy coupling Jingyue Wan(万婧玥), Fuqiang Wu(吴富强), Jun Ma(马军), and Wenshuai Wang(汪文帅). Chin. Phys. B, 2024, 33(5): 050504.
[3]	Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model Xinwei Zhou(周新卫), Donghua Jiang(蒋东华), Jean De Dieu Nkapkop, Musheer Ahmad, Jules Tagne Fossi, Nestor Tsafack, and Jianhua Wu(吴建华). Chin. Phys. B, 2024, 33(4): 040506.
[4]	Dynamical behaviors in discrete memristor-coupled small-world neuronal networks Jieyu Lu(鲁婕妤), Xiaohua Xie(谢小华), Yaping Lu(卢亚平), Yalian Wu(吴亚联), Chunlai Li(李春来), and Minglin Ma(马铭磷). Chin. Phys. B, 2024, 33(4): 048701.
[5]	Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption Xiaoxia Li(李晓霞), Qianqian He(何倩倩), Tianyi Yu(余天意),Zhuang Cai(才壮), and Guizhi Xu(徐桂芝). Chin. Phys. B, 2024, 33(3): 030505.
[6]	Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk Minglin Ma(马铭磷), Kangling Xiong(熊康灵), Zhijun Li(李志军), and Shaobo He(贺少波). Chin. Phys. B, 2024, 33(2): 028706.
[7]	Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise Xun Yan(晏询), Zhijun Li(李志军), and Chunlai Li(李春来). Chin. Phys. B, 2024, 33(2): 028705.
[8]	Dynamical analysis, geometric control and digital hardware implementation of a complex-valued laser system with a locally active memristor Yi-Qun Li(李逸群), Jian Liu(刘坚), Chun-Biao Li(李春彪), Zhi-Feng Hao(郝志峰), and Xiao-Tong Zhang(张晓彤). Chin. Phys. B, 2023, 32(8): 080503.
[9]	Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing Peiwen Tong(童霈文), Hui Xu(徐晖), Yi Sun(孙毅), Yongzhou Wang(汪泳州), Jie Peng(彭杰),Cen Liao(廖岑), Wei Wang(王伟), and Qingjiang Li(李清江). Chin. Phys. B, 2023, 32(7): 078505.
[10]	A progressive surrogate gradient learning for memristive spiking neural network Shu Wang(王姝), Tao Chen(陈涛), Yu Gong(龚钰), Fan Sun(孙帆), Si-Yuan Shen(申思远), Shu-Kai Duan(段书凯), and Li-Dan Wang(王丽丹). Chin. Phys. B, 2023, 32(6): 068704.
[11]	Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor Ming-Lin Ma(马铭磷), Xiao-Hua Xie(谢小华), Yang Yang(杨阳), Zhi-Jun Li(李志军), and Yi-Chuang Sun(孙义闯). Chin. Phys. B, 2023, 32(5): 058701.
[12]	Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh-Rose and FitzHugh-Nagumo neurons with two time delays Zhan-Hong Guo(郭展宏), Zhi-Jun Li(李志军), Meng-Jiao Wang(王梦蛟), and Ming-Lin Ma(马铭磷). Chin. Phys. B, 2023, 32(3): 038701.
[13]	Memristor's characteristics: From non-ideal to ideal Fan Sun(孙帆), Jing Su(粟静), Jie Li(李杰), Shukai Duan(段书凯), and Xiaofang Hu(胡小方). Chin. Phys. B, 2023, 32(2): 028401.
[14]	Optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system and compressed sensing Yang Du(都洋), Guoqiang Long(隆国强), Donghua Jiang(蒋东华), Xiuli Chai(柴秀丽), and Junhe Han(韩俊鹤). Chin. Phys. B, 2023, 32(11): 114203.
[15]	Rucklidge-based memristive chaotic system: Dynamic analysis and image encryption Can-Ling Jian(蹇璨岭), Ze-An Tian(田泽安), Bo Liang(梁波), Chen-Yang Hu(胡晨阳), Qiao Wang(王桥), and Jing-Xi Chen(陈靖翕). Chin. Phys. B, 2023, 32(10): 100503.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

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

Cite this article:

Online attention