Hsin-Jung Yang, Kermin Fleming, F. Winterstein, Annie I. Chen, Michael Adler, J. Emer
{"title":"程序优化FPGA存储网络的自动构建","authors":"Hsin-Jung Yang, Kermin Fleming, F. Winterstein, Annie I. Chen, Michael Adler, J. Emer","doi":"10.1145/3020078.3021748","DOIUrl":null,"url":null,"abstract":"Memory systems play a key role in the performance of FPGA applications. As FPGA deployments move towards design entry points that are more serial, memory latency has become a serious design consideration. For these applications, memory network optimization is essential in improving performance. In this paper, we examine the automatic, program-optimized construction of low-latency memory networks. We design a feedback-driven network compiler, which constructs an optimized memory network based on the target program's memory access behavior measured via a newly designed network profiler. In our test applications, the compiler-optimized networks provide a 45% performance gain on average over baseline memory networks by minimizing the impact of network latency on program performance.","PeriodicalId":252039,"journal":{"name":"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automatic Construction of Program-Optimized FPGA Memory Networks\",\"authors\":\"Hsin-Jung Yang, Kermin Fleming, F. Winterstein, Annie I. Chen, Michael Adler, J. Emer\",\"doi\":\"10.1145/3020078.3021748\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Memory systems play a key role in the performance of FPGA applications. As FPGA deployments move towards design entry points that are more serial, memory latency has become a serious design consideration. For these applications, memory network optimization is essential in improving performance. In this paper, we examine the automatic, program-optimized construction of low-latency memory networks. We design a feedback-driven network compiler, which constructs an optimized memory network based on the target program's memory access behavior measured via a newly designed network profiler. In our test applications, the compiler-optimized networks provide a 45% performance gain on average over baseline memory networks by minimizing the impact of network latency on program performance.\",\"PeriodicalId\":252039,\"journal\":{\"name\":\"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3020078.3021748\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3020078.3021748","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Construction of Program-Optimized FPGA Memory Networks
Memory systems play a key role in the performance of FPGA applications. As FPGA deployments move towards design entry points that are more serial, memory latency has become a serious design consideration. For these applications, memory network optimization is essential in improving performance. In this paper, we examine the automatic, program-optimized construction of low-latency memory networks. We design a feedback-driven network compiler, which constructs an optimized memory network based on the target program's memory access behavior measured via a newly designed network profiler. In our test applications, the compiler-optimized networks provide a 45% performance gain on average over baseline memory networks by minimizing the impact of network latency on program performance.