{"title":"基于模拟存储器的图卷积网络的实现","authors":"Daqin Chen, Zongwei Wang, Shengyu Bao, Yimao Cai, Ru Huang","doi":"10.1109/CSTIC49141.2020.9282441","DOIUrl":null,"url":null,"abstract":"In this work, the implementation of Graph Convolutional Network (GCN) based on resistive switching memory is demonstrated through simulation. After training, the RRAM-based GCN can process a semi-supervised graph classification task. Further, the impacts of read noises and circuit bit-precision on the performance of GCN are analyzed. Results show the proposed GCN can reach high accuracy when bit-precisions; 4-bit. Moreover, read noise can severely affect accuracy.","PeriodicalId":6848,"journal":{"name":"2020 China Semiconductor Technology International Conference (CSTIC)","volume":"46 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Implementation of Graph Convolution Network Based on Analog Rram\",\"authors\":\"Daqin Chen, Zongwei Wang, Shengyu Bao, Yimao Cai, Ru Huang\",\"doi\":\"10.1109/CSTIC49141.2020.9282441\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, the implementation of Graph Convolutional Network (GCN) based on resistive switching memory is demonstrated through simulation. After training, the RRAM-based GCN can process a semi-supervised graph classification task. Further, the impacts of read noises and circuit bit-precision on the performance of GCN are analyzed. Results show the proposed GCN can reach high accuracy when bit-precisions; 4-bit. Moreover, read noise can severely affect accuracy.\",\"PeriodicalId\":6848,\"journal\":{\"name\":\"2020 China Semiconductor Technology International Conference (CSTIC)\",\"volume\":\"46 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 China Semiconductor Technology International Conference (CSTIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSTIC49141.2020.9282441\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 China Semiconductor Technology International Conference (CSTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSTIC49141.2020.9282441","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of Graph Convolution Network Based on Analog Rram
In this work, the implementation of Graph Convolutional Network (GCN) based on resistive switching memory is demonstrated through simulation. After training, the RRAM-based GCN can process a semi-supervised graph classification task. Further, the impacts of read noises and circuit bit-precision on the performance of GCN are analyzed. Results show the proposed GCN can reach high accuracy when bit-precisions; 4-bit. Moreover, read noise can severely affect accuracy.
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