{"title":"云中的fpga","authors":"G. Constantinides","doi":"10.1145/3020078.3030014","DOIUrl":null,"url":null,"abstract":"Ever greater amounts of computing and storage are happening remotely in the cloud, and it is estimated that spending on public cloud services will grow by over 19%/year to $140B in 2019. Besides commodity processors, network and storage infrastructure, the end of clock frequency scaling in traditional processors has meant that application-specific accelerators are required in tandem with cloud-based processors to deliver continued improvements in computational performance and energy efficiency. Indeed, graphics processing units (GPUs), as well as custom ASICs, are now widely used within the cloud, particularly for compute-intensive high-value applications like machine learning. In this panel, we intend to consider the opportunities and challenges for broad deployment of FPGAs in the cloud.","PeriodicalId":252039,"journal":{"name":"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"FPGAs in the Cloud\",\"authors\":\"G. Constantinides\",\"doi\":\"10.1145/3020078.3030014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ever greater amounts of computing and storage are happening remotely in the cloud, and it is estimated that spending on public cloud services will grow by over 19%/year to $140B in 2019. Besides commodity processors, network and storage infrastructure, the end of clock frequency scaling in traditional processors has meant that application-specific accelerators are required in tandem with cloud-based processors to deliver continued improvements in computational performance and energy efficiency. Indeed, graphics processing units (GPUs), as well as custom ASICs, are now widely used within the cloud, particularly for compute-intensive high-value applications like machine learning. In this panel, we intend to consider the opportunities and challenges for broad deployment of FPGAs in the cloud.\",\"PeriodicalId\":252039,\"journal\":{\"name\":\"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3020078.3030014\",\"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 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3020078.3030014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

越来越多的计算和存储正在远程云中进行,据估计,到2019年,公共云服务的支出将以每年19%以上的速度增长,达到1400亿美元。除了商用处理器、网络和存储基础设施之外,传统处理器时钟频率缩放的终结意味着特定应用的加速器需要与基于云的处理器协同工作,以不断提高计算性能和能源效率。事实上,图形处理单元(gpu)以及定制asic现在在云中被广泛使用,特别是在机器学习等计算密集型高价值应用中。在这个小组中,我们打算考虑在云中广泛部署fpga的机遇和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
FPGAs in the Cloud
Ever greater amounts of computing and storage are happening remotely in the cloud, and it is estimated that spending on public cloud services will grow by over 19%/year to $140B in 2019. Besides commodity processors, network and storage infrastructure, the end of clock frequency scaling in traditional processors has meant that application-specific accelerators are required in tandem with cloud-based processors to deliver continued improvements in computational performance and energy efficiency. Indeed, graphics processing units (GPUs), as well as custom ASICs, are now widely used within the cloud, particularly for compute-intensive high-value applications like machine learning. In this panel, we intend to consider the opportunities and challenges for broad deployment of FPGAs in the cloud.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Session details: CAD Tools CPU-FPGA Co-Optimization for Big Data Applications: A Case Study of In-Memory Samtool Sorting (Abstract Only) Session details: Graph Processing Applications ASAP: Accelerated Short Read Alignment on Programmable Hardware (Abstract Only) Learning Convolutional Neural Networks for Data-Flow Graph Mapping on Spatial Programmable Architectures (Abstract Only)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1