Quantized and adaptive memristor based CNN(QA-mCNN)for image processing  

作　　者：Xiaofang HU Wenqiang SHI Yue ZHOU Hongan TANG Shukai DUAN 

机构地区：[1]College of Artificial Intelligence,Southwest University,Chongqing 400715,China [2]College of Computer and Information Science,Southwest University,Chongqing 400715,China [3]School of Mathematics and Statistics,Southwest University,Chongqing 400715,China [4]College of Electronics and Information Engineering,Southwest University,Chongqing 400715,China [5]School of Artificial Intelligence,Chongqing University of Technology,Chongqing 401135,China

出　　处：《Science China(Information Sciences)》2022年第1期269-271,共3页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61976246,61601376);Natural Science Foundation of Chongqing(Grant No.cstc2020jcyj-msxm0016);National Key R&D Program of China(Grant No.2018YFB1306604);China Postdoctoral Science Foundation Special Funded(Grant No.2018T110937);Innovation Support Program for Chongqing Overseas Returnees(Grant No.cx2019126);Zhejiang Provincial Key Lab of Equipment Electronics(Grant No.2019E10009)。

摘　　要：Dear editor,The cellular neural network(CNN)is one of the most hardware-implementable neural networks with promising prospects on large-scale and real-time problem solving[1].However,the lack of adaptive templates and advanced implementation technology has hindered its developments and practical applications.

关 键 词：network NEURAL image 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]