FCCM 2019特别部分介绍

A. DeHon
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引用次数: 0

摘要

现场可编程定制计算机国际研讨会(FCCM)是展示和讨论与利用现场可编程门阵列(fpga)和其他可重构硬件的独特特征和能力的计算相关的新研究的原始和首要论坛。自1993年第一次会议以来,FCCM一直是展示现场可编程定制计算机器以及使用这些系统的应用程序在体系结构、工具和编程模型方面的最新工作的地方。第27届FCCM于2019年4月28日至5月1日在加州圣地亚哥的加州大学圣地亚哥分校举行。在这个特别的ACM TRETS部分,我们很高兴地介绍在FCCM 2019上接受和发表的31篇全文和7篇短文中的2篇精选的扩展版本。随着处理器频率缩放和Dennard缩放的终结,更广泛的架构社区开始认识到“加速器”和应用定制并行计算引擎的作用。FCCM在其整个历史中都强调并培养了这些应用定制加速器的知识体系和工具。在这个特殊的部分中,我们提供了两篇文章,提供了解决大规模fpga和计算系统中日益增长的挑战的工具。他们的代码都是开源的,因此他们也为技术基础设施和构建模块做出了贡献,使我们的社区能够协作推进该领域的发展。阻碍FPGA计算的常见抱怨之一是基本映射工具的运行时很慢。与处理器和gpu相比,FPGA映射长达一小时的运行时间不利于潜在的采用者。随着FPGA的容量随着摩尔定律的增长,FPGA的映射时间也在持续增长。路由通常是FPGA的主要组成部分之一
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introduction to Special Section on FCCM 2019
The International Symposium on Field-Programmable Custom Computing Machines (FCCM) is the original and premier forum for presenting and discussing new research related to computing that exploits the unique features and capabilities of field-programmable gate arrays (FPGAs) and other reconfigurable hardware. Since its first meeting in 1993, FCCM has been the place to present the latest work in architectures, tools, and programming models for field-programmable custom computing machines as well as applications that use such systems. The 27th FCCM met April 28 to May 1, 2019 on the campus of UCSD in San Diego, California. In this special ACM TRETS section, it is our pleasure to present extended versions of a selected set of 2 of the 31 full and 7 short papers accepted and presented at FCCM 2019. With the end of processor frequency scaling and the end of Dennard Scaling, the broader architecture community is starting to acknowledge the role of “accelerators” and applicationcustomized parallel compute engines. FCCM has highlighted and nurtured the body-of-knowledge and tools for these application-customized accelerators throughout its history. In this special section, we present two article that provide tools to address the growing challenges in large-scale FPGAs and computing systems. Both come with open-source releases for their code so they also contribute to the technical infrastructure and building blocks that allow our community to collaboratively advance the field. One of the common complaints holding back FPGA computing is the slow runtime for the basic mapping tools. Compared to processors and GPUs, the hour-long runtimes for FPGA mapping serve to discourage potential adopters. As FPGAs grow in capacity with Moore’s Law, FPGA mapping times have continued to grow. Routing is often one of the large components of FPGA
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