N. Kroupis, N. Zervas, M. Dasygenis, K. Tatas, D. Soudris, A. Thanailakis
{"title":"在可编程处理器上实现DSP应用的高水平性能和功耗探索","authors":"N. Kroupis, N. Zervas, M. Dasygenis, K. Tatas, D. Soudris, A. Thanailakis","doi":"10.1109/ISSPIT.2005.1577217","DOIUrl":null,"url":null,"abstract":"The continuous increase of the computational power of programmable processors has established them as an attractive design alternative, for implementation of the most computationally intensive applications, like video compression. To enforce this trend, designers implementing applications on programmable platforms have to be provided with reliable and in-depth analysis data that will allow for the early selection of the most appropriate application for a given set of specifications. To address this need, we introduce a new methodology for early and accurate estimation of the number of instructions required for the execution of an application, together with the number of data memory transfers on a programmable processor. The high-level estimation is achieved by a series of mathematical formulas; these describe not only the arithmetic operations of an application, but also its control and addressing operations, if it is executed on a programmable core. The comparative study, which is done using three popular processors (Pentium, ARM and MIPS), shows the high efficiency and accuracy of the methodology proposed, in terms of the number of executed (micro-)instructions (i.e. performance) and the number of data memory transfers (i.e. memory energy consumption)","PeriodicalId":421826,"journal":{"name":"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-level performance and power exploration of DSP applications realized on programmable processors\",\"authors\":\"N. Kroupis, N. Zervas, M. Dasygenis, K. Tatas, D. Soudris, A. Thanailakis\",\"doi\":\"10.1109/ISSPIT.2005.1577217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The continuous increase of the computational power of programmable processors has established them as an attractive design alternative, for implementation of the most computationally intensive applications, like video compression. To enforce this trend, designers implementing applications on programmable platforms have to be provided with reliable and in-depth analysis data that will allow for the early selection of the most appropriate application for a given set of specifications. To address this need, we introduce a new methodology for early and accurate estimation of the number of instructions required for the execution of an application, together with the number of data memory transfers on a programmable processor. The high-level estimation is achieved by a series of mathematical formulas; these describe not only the arithmetic operations of an application, but also its control and addressing operations, if it is executed on a programmable core. The comparative study, which is done using three popular processors (Pentium, ARM and MIPS), shows the high efficiency and accuracy of the methodology proposed, in terms of the number of executed (micro-)instructions (i.e. performance) and the number of data memory transfers (i.e. memory energy consumption)\",\"PeriodicalId\":421826,\"journal\":{\"name\":\"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2005.1577217\",\"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 Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2005.1577217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High-level performance and power exploration of DSP applications realized on programmable processors
The continuous increase of the computational power of programmable processors has established them as an attractive design alternative, for implementation of the most computationally intensive applications, like video compression. To enforce this trend, designers implementing applications on programmable platforms have to be provided with reliable and in-depth analysis data that will allow for the early selection of the most appropriate application for a given set of specifications. To address this need, we introduce a new methodology for early and accurate estimation of the number of instructions required for the execution of an application, together with the number of data memory transfers on a programmable processor. The high-level estimation is achieved by a series of mathematical formulas; these describe not only the arithmetic operations of an application, but also its control and addressing operations, if it is executed on a programmable core. The comparative study, which is done using three popular processors (Pentium, ARM and MIPS), shows the high efficiency and accuracy of the methodology proposed, in terms of the number of executed (micro-)instructions (i.e. performance) and the number of data memory transfers (i.e. memory energy consumption)