High-Precision Symmetric Weight Update of Memristor by Gate Voltage Ramping Method for Convolutional Neural Network Accelerator

被引：37

作者：

Chen, Jia ^{[1
]}

Pan, Wen-Qian ^{[1
]}

Li, Yi ^{[1
]}

Kuang, Rui ^{[1
]}

He, Yu-Hui ^{[1
]}

Lin, Chih-Yang ^{[2
]}

Duan, Nian ^{[1
]}

Feng, Gui-Rong ^{[1
]}

Zheng, Hao-Xuan ^{[2
]}

Chang, Ting-Chang ^{[2
]}

Sze, Simon M. ^{[3
]}

Miao, Xiang-Shui ^{[1
]}

机构：

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

[2] Natl Sun Yat Sen Univ, Dept Phys, Kaohsiung 80424, Taiwan

[3] Natl Chiao Tung Univ, Dept Elect Engn, Hsinchu 300, Taiwan

来源：

IEEE ELECTRON DEVICE LETTERS | 2020年 / 41卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Memristor; symmetric weight update; convolutional neural network; MEMORY; CLASSIFICATION;

D O I：

10.1109/LED.2020.2968388

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：

Memristor emerges as the key enabler for neural network accelerator. Here, we demonstrate high-precision symmetric weight update in a one transistor one resistor (1T1R) structure Ti/HfO2/TiN memristor using a gate voltage ramping method, with over 120-level states and low variation (< 4%). Incorporating all experimental non-idealities, the proposed mixed hardware-software convolutional neural network demonstrates over 92.79% online learning accuracy (against software equivalent 98.45%) for MNIST recognition task. The network also shows robustness to input image noises, array yield, and retention issues.

引用

页码：353 / 356

页数：4

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