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Titlebook: Emerging Technology and Architecture for Big-data Analytics; Anupam Chattopadhyay,Chip Hong Chang,Hao Yu Book 2017 Springer International

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書目名稱Emerging Technology and Architecture for Big-data Analytics
編輯Anupam Chattopadhyay,Chip Hong Chang,Hao Yu
視頻videohttp://file.papertrans.cn/309/308464/308464.mp4
概述Provides a single-source reference to hardware architectures for big-data analytics.Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and syste
圖書封面Titlebook: Emerging Technology and Architecture for Big-data Analytics;  Anupam Chattopadhyay,Chip Hong Chang,Hao Yu Book 2017 Springer International
描述.This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities..Provides a single-source reference to hardware architectures for big-data analytics;.Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems;.Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics..
出版日期Book 2017
關(guān)鍵詞Big Data Analytics; Real-Time Big Data Analytics; next-generation data analytics; exascale computing; no
版次1
doihttps://doi.org/10.1007/978-3-319-54840-1
isbn_softcover978-3-319-85497-7
isbn_ebook978-3-319-54840-1
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Emerging Technology and Architecture for Big-data Analytics978-3-319-54840-1
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https://doi.org/10.1007/978-3-322-96007-8r architectures resulting in a compact and energy-efficient “in-memory computing” platform. In this chapter, we will review spintronic device structures consisting of single-domain/domain-wall motion based devices for mimicking neuronal and synaptic units. System-level simulations indicate?~?100× im
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its and systems;.Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics..978-3-319-85497-7978-3-319-54840-1
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Accelerating Data Analytics Kernels with Heterogeneous ComputingHLS-based exhaustive architectural exploration for implementation of the kernels practically infeasible. To address this challenge, we have developed Lin-Analyzer, a high-level accurate performance analysis tool that enables rapid design space exploration with various pragmas for FPGA-based accelera
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