{"title":"基于HLS的LS-SVM在Zynq上的实现","authors":"Ma Ning, Wang Shaojun, Pang Yeyong, Peng Yu","doi":"10.1109/FPT.2014.7082816","DOIUrl":null,"url":null,"abstract":"In recent years, implementing a complicated algorithm in an embedded system, especially in a heterogeneous computing system, has gained more and more attention in many fields. The problem is that the implementation needs amounts of coding and debugging work, even if the algorithm has been verified by high-level language in PC environment. Our demo presents a method which can reduce the time of developing an algorithm in an embedded and heterogeneous system by high level synthesis method. Least Square Support Vector Machine(LS-SVM) algorithm was realized on Zynq platform by translating high-level language to Hardware Description Language(HDL). Basing on the feature of the developed heterogeneous system and the theory of LS-SVM, three parts were implemented to realize LS-SVM which includes a generating Kernel Matrix module, a solving linear equations module and a forecasting module. The first and the third parts have been placed in ARM processor by C language. Moreover, considering that the second parts was compute-intensive, it has been realized in logic resource by using high-level language. To manage data communication and computing task, an SOPC system has been designed on Zynq platform which worked in PXI chassis. Experiments demonstrate that the design method is feasible and can be used for the implementation of other complicate algorithm. The precision and time consumption in computing are given at the end.","PeriodicalId":6877,"journal":{"name":"2014 International Conference on Field-Programmable Technology (FPT)","volume":"81 1","pages":"346-349"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Implementation of LS-SVM with HLS on Zynq\",\"authors\":\"Ma Ning, Wang Shaojun, Pang Yeyong, Peng Yu\",\"doi\":\"10.1109/FPT.2014.7082816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, implementing a complicated algorithm in an embedded system, especially in a heterogeneous computing system, has gained more and more attention in many fields. The problem is that the implementation needs amounts of coding and debugging work, even if the algorithm has been verified by high-level language in PC environment. Our demo presents a method which can reduce the time of developing an algorithm in an embedded and heterogeneous system by high level synthesis method. Least Square Support Vector Machine(LS-SVM) algorithm was realized on Zynq platform by translating high-level language to Hardware Description Language(HDL). Basing on the feature of the developed heterogeneous system and the theory of LS-SVM, three parts were implemented to realize LS-SVM which includes a generating Kernel Matrix module, a solving linear equations module and a forecasting module. The first and the third parts have been placed in ARM processor by C language. Moreover, considering that the second parts was compute-intensive, it has been realized in logic resource by using high-level language. To manage data communication and computing task, an SOPC system has been designed on Zynq platform which worked in PXI chassis. Experiments demonstrate that the design method is feasible and can be used for the implementation of other complicate algorithm. The precision and time consumption in computing are given at the end.\",\"PeriodicalId\":6877,\"journal\":{\"name\":\"2014 International Conference on Field-Programmable Technology (FPT)\",\"volume\":\"81 1\",\"pages\":\"346-349\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Field-Programmable Technology (FPT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FPT.2014.7082816\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Field-Programmable Technology (FPT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPT.2014.7082816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In recent years, implementing a complicated algorithm in an embedded system, especially in a heterogeneous computing system, has gained more and more attention in many fields. The problem is that the implementation needs amounts of coding and debugging work, even if the algorithm has been verified by high-level language in PC environment. Our demo presents a method which can reduce the time of developing an algorithm in an embedded and heterogeneous system by high level synthesis method. Least Square Support Vector Machine(LS-SVM) algorithm was realized on Zynq platform by translating high-level language to Hardware Description Language(HDL). Basing on the feature of the developed heterogeneous system and the theory of LS-SVM, three parts were implemented to realize LS-SVM which includes a generating Kernel Matrix module, a solving linear equations module and a forecasting module. The first and the third parts have been placed in ARM processor by C language. Moreover, considering that the second parts was compute-intensive, it has been realized in logic resource by using high-level language. To manage data communication and computing task, an SOPC system has been designed on Zynq platform which worked in PXI chassis. Experiments demonstrate that the design method is feasible and can be used for the implementation of other complicate algorithm. The precision and time consumption in computing are given at the end.