{"title":"超低功耗mpsoc的共享fpu架构","authors":"M. R. Kakoee, Igor Loi, L. Benini","doi":"10.1145/2482767.2482772","DOIUrl":null,"url":null,"abstract":"In this work we propose a shared floating point unit (FPU) architecture for ultra low power (ULP) system on chips operating at near threshold voltage (NTV). Since high-performance FP units (FPUs) are large and complex, but their utilization is relatively low, adding one FPU per each core in a ULP multicore is costly and power hungry. In our approach, we share a few FPUs among all the cores in the system. This increases the utilization of FPUs leading to an energy-efficient design. As a part of our approach, we propose two different FPU allocation techniques: optimal and random.\n Experimental results demonstrate that compared to a traditional private-FPU approach, our technique in a multicore system with 8 processors and 2 shared FPUs can increase the performance/(area*power) by 5x for applications with 10% FP operations and by 2.5x for applications with 25% FP operations.","PeriodicalId":430420,"journal":{"name":"ACM International Conference on Computing Frontiers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A shared-FPU architecture for ultra-low power MPSoCs\",\"authors\":\"M. R. Kakoee, Igor Loi, L. Benini\",\"doi\":\"10.1145/2482767.2482772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we propose a shared floating point unit (FPU) architecture for ultra low power (ULP) system on chips operating at near threshold voltage (NTV). Since high-performance FP units (FPUs) are large and complex, but their utilization is relatively low, adding one FPU per each core in a ULP multicore is costly and power hungry. In our approach, we share a few FPUs among all the cores in the system. This increases the utilization of FPUs leading to an energy-efficient design. As a part of our approach, we propose two different FPU allocation techniques: optimal and random.\\n Experimental results demonstrate that compared to a traditional private-FPU approach, our technique in a multicore system with 8 processors and 2 shared FPUs can increase the performance/(area*power) by 5x for applications with 10% FP operations and by 2.5x for applications with 25% FP operations.\",\"PeriodicalId\":430420,\"journal\":{\"name\":\"ACM International Conference on Computing Frontiers\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM International Conference on Computing Frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2482767.2482772\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2482767.2482772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A shared-FPU architecture for ultra-low power MPSoCs
In this work we propose a shared floating point unit (FPU) architecture for ultra low power (ULP) system on chips operating at near threshold voltage (NTV). Since high-performance FP units (FPUs) are large and complex, but their utilization is relatively low, adding one FPU per each core in a ULP multicore is costly and power hungry. In our approach, we share a few FPUs among all the cores in the system. This increases the utilization of FPUs leading to an energy-efficient design. As a part of our approach, we propose two different FPU allocation techniques: optimal and random.
Experimental results demonstrate that compared to a traditional private-FPU approach, our technique in a multicore system with 8 processors and 2 shared FPUs can increase the performance/(area*power) by 5x for applications with 10% FP operations and by 2.5x for applications with 25% FP operations.