{"title":"FiT: An Automated Toolkit for Matching Processor Architecture to Applications (Abstract Only)","authors":"C. Mutigwe, J. Kinyua, F. Aghdasi","doi":"10.1145/2684746.2689117","DOIUrl":null,"url":null,"abstract":"As the complexity of designing electronic systems continues to grow, the most commonly used solution has been to move the design process to higher levels of abstraction via software tools. In this work we present one such tool that can be used to automatically generate custom processors and systems-on-chip (SoC) from C source code or application binary files, with no requirement for the user to understand any of the underlying hardware systems. This tool also does not call for the application to be profiled for any 'hot spots' as a prerequisite for generating the custom processor. We use the toolkit to generate two types of custom processors; the area-optimized processors and the performance-optimized processors. We study the resource utilization of the custom processors and compare them with those predicted by the core density model. We find that the performance-optimized processor results are as predicted by the core density model.","PeriodicalId":388546,"journal":{"name":"Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2684746.2689117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
As the complexity of designing electronic systems continues to grow, the most commonly used solution has been to move the design process to higher levels of abstraction via software tools. In this work we present one such tool that can be used to automatically generate custom processors and systems-on-chip (SoC) from C source code or application binary files, with no requirement for the user to understand any of the underlying hardware systems. This tool also does not call for the application to be profiled for any 'hot spots' as a prerequisite for generating the custom processor. We use the toolkit to generate two types of custom processors; the area-optimized processors and the performance-optimized processors. We study the resource utilization of the custom processors and compare them with those predicted by the core density model. We find that the performance-optimized processor results are as predicted by the core density model.