Seyyed Mahdi Najmabadi, Zhe Wang, Y. Baroud, S. Simon
{"title":"Online Bandwidth Reduction Using Dynamic Partial Reconfiguration","authors":"Seyyed Mahdi Najmabadi, Zhe Wang, Y. Baroud, S. Simon","doi":"10.1109/FCCM.2016.49","DOIUrl":null,"url":null,"abstract":"Online compression of I/O-data streams in Custom Computing Machines will enhance the effective network band-width of computing systems as well as storage bandwidth and capacity. In this paper a self-adaptive dynamic partial reconfigurable architecture for online compression is proposed. The proposed architecture will bring new possibilities in online compression due to its adaptivity to dynamic environments. In this study, network traffic, and input data distribution are considered as two dynamic behaviors. The degree of improvement provided by the architecture depends on data distribution, bandwidth, and available resources. Our analysis shows an improvement of up to 20% in compression ratios in comparison to non-adaptive approaches.","PeriodicalId":113498,"journal":{"name":"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FCCM.2016.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Online compression of I/O-data streams in Custom Computing Machines will enhance the effective network band-width of computing systems as well as storage bandwidth and capacity. In this paper a self-adaptive dynamic partial reconfigurable architecture for online compression is proposed. The proposed architecture will bring new possibilities in online compression due to its adaptivity to dynamic environments. In this study, network traffic, and input data distribution are considered as two dynamic behaviors. The degree of improvement provided by the architecture depends on data distribution, bandwidth, and available resources. Our analysis shows an improvement of up to 20% in compression ratios in comparison to non-adaptive approaches.
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