MEDICS:用于医学图像重建的超便携处理

Ganesh S. Dasika, Ankit Sethia, Vincentius Robby, T. Mudge, S. Mahlke
{"title":"MEDICS:用于医学图像重建的超便携处理","authors":"Ganesh S. Dasika, Ankit Sethia, Vincentius Robby, T. Mudge, S. Mahlke","doi":"10.1145/1854273.1854299","DOIUrl":null,"url":null,"abstract":"Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically reduced. This can only be done, however, by using complex image reconstruction algorithms to correct artifacts introduced by low-power acquisition, resulting in image processing becoming the dominant power-consuming task. Current solutions use combinations of digital signal processors, general-purpose processors and, more recently, general-purpose graphics processing units for medical image processing. These solutions fall short for various reasons including high power consumption and an inability to execute the next generation of image reconstruction algorithms. This paper presents the MEDICS architecture - a domain-specific multicore architecture designed specifically for medical imaging applications, but with sufficient generality tomake it programmable. The goal is to achieve 100 GFLOPs of performance while consuming orders of magnitude less power than the existing solutions. MEDICS has a throughput of 128 GFLOPs while consuming as little as 1.6W of power on advanced CT reconstruction applications. This represents up to a 20X increase in computation efficiency over current designs.","PeriodicalId":422461,"journal":{"name":"2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"MEDICS: Ultra-portable processing for medical image reconstruction\",\"authors\":\"Ganesh S. Dasika, Ankit Sethia, Vincentius Robby, T. Mudge, S. Mahlke\",\"doi\":\"10.1145/1854273.1854299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically reduced. This can only be done, however, by using complex image reconstruction algorithms to correct artifacts introduced by low-power acquisition, resulting in image processing becoming the dominant power-consuming task. Current solutions use combinations of digital signal processors, general-purpose processors and, more recently, general-purpose graphics processing units for medical image processing. These solutions fall short for various reasons including high power consumption and an inability to execute the next generation of image reconstruction algorithms. This paper presents the MEDICS architecture - a domain-specific multicore architecture designed specifically for medical imaging applications, but with sufficient generality tomake it programmable. The goal is to achieve 100 GFLOPs of performance while consuming orders of magnitude less power than the existing solutions. MEDICS has a throughput of 128 GFLOPs while consuming as little as 1.6W of power on advanced CT reconstruction applications. This represents up to a 20X increase in computation efficiency over current designs.\",\"PeriodicalId\":422461,\"journal\":{\"name\":\"2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1854273.1854299\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1854273.1854299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

医学成像为医生提供了生成人体3D图像的能力,以便检测和诊断各种疾病。使医学成像便于携带和更容易获得提供了一系列独特的挑战。为了提高可移植性,图像采集所消耗的功率——目前成像设备中最耗电的活动——必须大幅降低。然而,这只能通过使用复杂的图像重建算法来纠正低功耗采集带来的伪影,从而导致图像处理成为主要的功耗任务。目前的解决方案使用数字信号处理器、通用处理器以及最近用于医学图像处理的通用图形处理单元的组合。这些解决方案由于各种原因而不足,包括高功耗和无法执行下一代图像重建算法。本文介绍了MEDICS体系结构——一个专门为医学成像应用设计的领域特定的多核体系结构,但具有足够的通用性,使其可编程。目标是实现100 GFLOPs的性能,同时消耗比现有解决方案少几个数量级的功率。MEDICS的吞吐量为128 GFLOPs,而在高级CT重建应用中功耗仅为1.6W。这意味着与当前设计相比,计算效率提高了20倍。
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
MEDICS: Ultra-portable processing for medical image reconstruction
Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically reduced. This can only be done, however, by using complex image reconstruction algorithms to correct artifacts introduced by low-power acquisition, resulting in image processing becoming the dominant power-consuming task. Current solutions use combinations of digital signal processors, general-purpose processors and, more recently, general-purpose graphics processing units for medical image processing. These solutions fall short for various reasons including high power consumption and an inability to execute the next generation of image reconstruction algorithms. This paper presents the MEDICS architecture - a domain-specific multicore architecture designed specifically for medical imaging applications, but with sufficient generality tomake it programmable. The goal is to achieve 100 GFLOPs of performance while consuming orders of magnitude less power than the existing solutions. MEDICS has a throughput of 128 GFLOPs while consuming as little as 1.6W of power on advanced CT reconstruction applications. This represents up to a 20X increase in computation efficiency over current designs.
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
Reducing task creation and termination overhead in explicitly parallel programs An intra-tile cache set balancing scheme NUcache: A multicore cache organization based on Next-Use distance Towards a science of parallel programming Discovering and understanding performance bottlenecks in transactional applications
×
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