A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition

Yuejun Zhang; Zhixin Wu; Shuzhi Liu; Zhecheng Guo; Qilai Chen; Pingqi Gao; Pengjun Wang; Gang Liu

doi:10.3389/fnins.2021.717222

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition

Front Neurosci. 2021 Sep 16:15:717222. doi: 10.3389/fnins.2021.717222. eCollection 2021.

Authors

Yuejun Zhang¹, Zhixin Wu^{1

2}, Shuzhi Liu², Zhecheng Guo¹, Qilai Chen^{2

3}, Pingqi Gao³, Pengjun Wang⁴, Gang Liu²

Affiliations

¹ Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China.
² Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
³ School of Materials, Sun Yat-sen University, Guangzhou, China.
⁴ College of Mathematics, Physics, and Electronic Information Engineering, Wenzhou University, Wenzhou, China.

Abstract

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.

Keywords: conductance fine-tuning; convolutional neural network; image denoising; memristor; synaptic plasticity.