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Titlebook: DataFlow Supercomputing Essentials; Research, Developmen Veljko Milutinovic,Jakob Salom,Luka Petrovic Book 2017 Springer International Publ

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發(fā)表于 2025-3-21 16:11:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱DataFlow Supercomputing Essentials
副標(biāo)題Research, Developmen
編輯Veljko Milutinovic,Jakob Salom,Luka Petrovic
視頻videohttp://file.papertrans.cn/264/263342/263342.mp4
概述Reviews the advantages of the DataFlow paradigm for supercomputing.Introduces the DataFlow programming model.Provides a selection of research analyses, implementation case studies, and tutorials on Da
叢書名稱Computer Communications and Networks
圖書封面Titlebook: DataFlow Supercomputing Essentials; Research, Developmen Veljko Milutinovic,Jakob Salom,Luka Petrovic Book 2017 Springer International Publ
描述.This informative text/reference highlights the potential of DataFlow computing in research requiring high speeds, low power requirements, and high precision, while also benefiting from a reduction in the size of the equipment. The cutting-edge research and implementation case studies provided in this book will help the reader to develop their practical understanding of the advantages and unique features of this methodology..This work serves as a companion title to?.DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations., which reviews the key algorithms in this area, and provides useful examples..Topics and features: reviews the library of tools, applications, and source code available to support DataFlow programming; discusses the enhancements to DataFlow computing yielded by small hardware changes, different compilation techniques, debugging, and optimizing tools; examines when a DataFlow architecture is best applied, and for which types of calculation; describes how converting applications to a DataFlow representation can result in an acceleration in performance, while reducing the power consumption; explains how to implement a DataFlow application on
出版日期Book 2017
關(guān)鍵詞DataFlow; Big Data; Supercomputing; Field-programmable gate array; Neural networks
版次1
doihttps://doi.org/10.1007/978-3-319-66128-5
isbn_softcover978-3-319-88184-3
isbn_ebook978-3-319-66128-5Series ISSN 1617-7975 Series E-ISSN 2197-8433
issn_series 1617-7975
copyrightSpringer International Publishing AG 2017
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Discrepancy Reduction Between the Topology of Dataflow Graph and the Topology of FPGA Structurexity/power/risks). The research analyzes the effects of one such infinitesimal add-on in the hardware domain (moving from two-input adders to three-input adders). Different compilation techniques, debugging and optimizing tools and methods, offered by “Maxeler Technologies Ltd.,” are presented and elaborated.
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Claire Bayntun MPH, MBBS, MSc, BScxity/power/risks). The research analyzes the effects of one such infinitesimal add-on in the hardware domain (moving from two-input adders to three-input adders). Different compilation techniques, debugging and optimizing tools and methods, offered by “Maxeler Technologies Ltd.,” are presented and elaborated.
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https://doi.org/10.1007/978-3-319-66128-5DataFlow; Big Data; Supercomputing; Field-programmable gate array; Neural networks
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978-3-319-88184-3Springer International Publishing AG 2017
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