Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, B. Gao, H. Qian, Huaqiang Wu
{"title":"基于松弛行为的卷积神经网络的RRAM保留预测框架","authors":"Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, B. Gao, H. Qian, Huaqiang Wu","doi":"10.1088/2634-4386/acb965","DOIUrl":null,"url":null,"abstract":"The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior\",\"authors\":\"Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, B. Gao, H. Qian, Huaqiang Wu\",\"doi\":\"10.1088/2634-4386/acb965\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.\",\"PeriodicalId\":198030,\"journal\":{\"name\":\"Neuromorphic Computing and Engineering\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuromorphic Computing and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2634-4386/acb965\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuromorphic Computing and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2634-4386/acb965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior
The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.