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

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 th...

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Autores principales: Zhang, Yuejun, Wu, Zhixin, Liu, Shuzhi, Guo, Zhecheng, Chen, Qilai, Gao, Pingqi, Wang, Pengjun, Liu, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481819/
https://www.ncbi.nlm.nih.gov/pubmed/34602968
http://dx.doi.org/10.3389/fnins.2021.717222
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author Zhang, Yuejun
Wu, Zhixin
Liu, Shuzhi
Guo, Zhecheng
Chen, Qilai
Gao, Pingqi
Wang, Pengjun
Liu, Gang
author_facet Zhang, Yuejun
Wu, Zhixin
Liu, Shuzhi
Guo, Zhecheng
Chen, Qilai
Gao, Pingqi
Wang, Pengjun
Liu, Gang
author_sort Zhang, Yuejun
collection PubMed
description 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.
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spelling pubmed-84818192021-10-01 A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition Zhang, Yuejun Wu, Zhixin Liu, Shuzhi Guo, Zhecheng Chen, Qilai Gao, Pingqi Wang, Pengjun Liu, Gang Front Neurosci Neuroscience 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. Frontiers Media S.A. 2021-09-16 /pmc/articles/PMC8481819/ /pubmed/34602968 http://dx.doi.org/10.3389/fnins.2021.717222 Text en Copyright © 2021 Zhang, Wu, Liu, Guo, Chen, Gao, Wang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhang, Yuejun
Wu, Zhixin
Liu, Shuzhi
Guo, Zhecheng
Chen, Qilai
Gao, Pingqi
Wang, Pengjun
Liu, Gang
A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title_full A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title_fullStr A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title_full_unstemmed A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title_short A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition
title_sort quantized convolutional neural network implemented with memristor for image denoising and recognition
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481819/
https://www.ncbi.nlm.nih.gov/pubmed/34602968
http://dx.doi.org/10.3389/fnins.2021.717222
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