{"title":"基于Dropout卷积神经网络的均值池模拟记忆系统架构","authors":"O. Krestinskaya, A. Bakambekova, A. P. James","doi":"10.1109/AICAS.2019.8771611","DOIUrl":null,"url":null,"abstract":"This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network\",\"authors\":\"O. Krestinskaya, A. Bakambekova, A. P. James\",\"doi\":\"10.1109/AICAS.2019.8771611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.\",\"PeriodicalId\":273095,\"journal\":{\"name\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICAS.2019.8771611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICAS.2019.8771611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network
This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.
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