{"title":"System-Level Performance Estimation for Application-Specific MPSoC Interconnect Synthesis","authors":"Po-Kuan Huang, Matin Hashemi, S. Ghiasi","doi":"10.1109/SASP.2008.4570792","DOIUrl":null,"url":null,"abstract":"We present a framework for development of streaming applications as concurrent software modules running on multi-processors system-on-chips (MPSoC). We propose an iterative design space exploration mechanism to customize MPSoC architecture for given applications. Central to the exploration engine is our system-level performance estimation methodology, that both quickly and accurately determine quality of candidate architectures. We implemented a number of streaming applications on candidate architectures that were emulated on an FPGA. Hardware measurements show that our system-level performance estimation method incurs only 15% error in predicting application throughput. More importantly, it always correctly guides design space exploration by achieving 100% fidelity in quality-ranking candidate architectures. Compared to behavioral simulation of compiled code, our system-level estimator runs more than 12 times faster, and requires 7 times less memory.","PeriodicalId":356441,"journal":{"name":"2008 Symposium on Application Specific Processors","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Symposium on Application Specific Processors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SASP.2008.4570792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
We present a framework for development of streaming applications as concurrent software modules running on multi-processors system-on-chips (MPSoC). We propose an iterative design space exploration mechanism to customize MPSoC architecture for given applications. Central to the exploration engine is our system-level performance estimation methodology, that both quickly and accurately determine quality of candidate architectures. We implemented a number of streaming applications on candidate architectures that were emulated on an FPGA. Hardware measurements show that our system-level performance estimation method incurs only 15% error in predicting application throughput. More importantly, it always correctly guides design space exploration by achieving 100% fidelity in quality-ranking candidate architectures. Compared to behavioral simulation of compiled code, our system-level estimator runs more than 12 times faster, and requires 7 times less memory.