{"title":"SimpleBP:用于有效设计探索的轻量级分支预测模拟器","authors":"Chaobing Zhou, Libo Huang, Zhisheng Li, Q. Dou","doi":"10.1109/NAS.2017.8026877","DOIUrl":null,"url":null,"abstract":"Besides the accuracy of prediction, chip area occupancy and power consumption also should be taken into account in the design of branch predictors. Many of the previous prediction simulation platforms have either only considered accuracy computed with coarse-grained updating model, or just been the low-speed full system simulators. In this paper, We presents SimpleBP, a lightweight prediction simulator based on trace driven. It leverages the SystemC language to simulate branch predictor at clock cycle granularity. And the CACTI model is introduced to evaluate area and power consumption. The experiment results show that SimpleBP can accurately give multiple evaluations of branch predictors.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SimpleBP: A Lightweight Branch Prediction Simulator for Effective Design Exploration\",\"authors\":\"Chaobing Zhou, Libo Huang, Zhisheng Li, Q. Dou\",\"doi\":\"10.1109/NAS.2017.8026877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Besides the accuracy of prediction, chip area occupancy and power consumption also should be taken into account in the design of branch predictors. Many of the previous prediction simulation platforms have either only considered accuracy computed with coarse-grained updating model, or just been the low-speed full system simulators. In this paper, We presents SimpleBP, a lightweight prediction simulator based on trace driven. It leverages the SystemC language to simulate branch predictor at clock cycle granularity. And the CACTI model is introduced to evaluate area and power consumption. The experiment results show that SimpleBP can accurately give multiple evaluations of branch predictors.\",\"PeriodicalId\":222161,\"journal\":{\"name\":\"2017 International Conference on Networking, Architecture, and Storage (NAS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Networking, Architecture, and Storage (NAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAS.2017.8026877\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Networking, Architecture, and Storage (NAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAS.2017.8026877","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SimpleBP: A Lightweight Branch Prediction Simulator for Effective Design Exploration
Besides the accuracy of prediction, chip area occupancy and power consumption also should be taken into account in the design of branch predictors. Many of the previous prediction simulation platforms have either only considered accuracy computed with coarse-grained updating model, or just been the low-speed full system simulators. In this paper, We presents SimpleBP, a lightweight prediction simulator based on trace driven. It leverages the SystemC language to simulate branch predictor at clock cycle granularity. And the CACTI model is introduced to evaluate area and power consumption. The experiment results show that SimpleBP can accurately give multiple evaluations of branch predictors.