High-Precision Symmetric Weight Update of Memristor by Gate Voltage Ramping Method for Convolutional Neural Network Accelerator
Jia Chen, Wen-Qian Pan, Yi Li, Rui Kuang, Yuhui He, Chih-Yang Lin, Nian Duan, Gui-Rong Feng, Hao-Xuan Zheng, Ting‐Chang Chang, Simon M. Sze, Xiangshui Miao
Abstract
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/HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /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.