Naive Bayes classifier based on memristor nonlinear conductance

被引：8

作者：

Li, Li ^{[1
]}

Zhou, Zuopai ^{[1
]}

Bai, Na ^{[1
]}

Wang, Tao ^{[1
]}

Xue, Kan-Hao ^{[1
,2
]}

Sun, Huajun ^{[1
,2
]}

He, Qiang ^{[1
,2
]}

Cheng, Weiming ^{[1
,2
]}

Miao, Xiangshui ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China

[2] Hubei Yangtze Memory Labs, Wuhan, Peoples R China

来源：

MICROELECTRONICS JOURNAL | 2022年 / 129卷

基金：

中国国家自然科学基金;

关键词：

Memristor; Naive bayes (NB); Processing-in-memory(PIM); VOLTAGE; MEMORY;

D O I：

10.1016/j.mejo.2022.105574

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this work,a naive Bayes classifier (NBC) based on memristor nonlinear conductance modulation is proposed, which not only can effectively avoid the influence of memristor nonlinearity and asymmetry on the network performance, but also enable on-chip training and inference completely on the memristive array. The perfor-mance of this classifier is evaluated by MNIST dataset classification, with highest recognition rate reaching 84.43%. In addition, the influence of other non-ideal factors of the memristor on the classification performance is analyzed, and a method to improve the classifier through pruning processing is proposed. The simulation proves that the improved selection Bayesian classifier (SBC) has a higher tolerance to the non-ideal factors of the memristor than the NBC.

引用

页数：9

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