LEADER 03236cmm 2200205 a 450

001 266853

008 170420s2017 gw s 0 eng d

020 $a9783319548401 (ebook)

020 $a9783319548395 (paper)

035 $a00313811

041 0 $aeng

082 04$a005.7$223

090 $aE-BOOK/005.7///UE017404

245 00$aEmerging technology and architecture for big-data analytics$h[electronic resource] /$cedited by Anupam Chattopadhyay, Chip Hong Chang, Hao Yu.

260 $aCham :$bSpringer International Publishing :$bImprint: Springer,$c2017.

300 $axi, 330 p. :$bill. (some col.), digital ;$c24 cm.

505 0 $aPart I State-of-the-Art Architectures and Automation for Data-analytics -- Chapter 1. Scaling the Java Virtual Machine on a Many-core System -- Chapter 2.Scaling the Java Virtual Machine on a Many-core System -- Chapter 3.Least-squares based Machine Learning Accelerator for Big-data Analytics in Smart Buildings -- Chapter 4.Compute-in-memory Architecture for Data-Intensive Kernels -- Chapter 5. New Solutions for Cross-Layer System-Level and High-Level Synthesis -- Part II New Solutions for Cross-Layer System-Level and High-Level Synthesis -- Chapter 6.Side Channel Attacks and Efficient Countermeasures on Residue Number System Multipliers -- Chapter 7. Ultra-Low-Power Biomedical Circuit Design and Optimization: Catching The Don't Cares -- Chapter 8.Acceleration of MapReduce Framework on a Multicore Processor -- Chapter 9. Adaptive dynamic range compression for improving envelope-based speech perception: Implications for cochlear implants -- Part III Emerging Technology, Circuits and Systems for Data-analytics -- Chapter 10. Emerging Technology, Circuits and Systems for Data-analytics -- Chapter 11. Energy Efficient Spiking Neural Network Design with RRAM Devices -- Chapter 12. Efficient Neuromorphic Systems and Emerging Technologies - Prospects and Perspectives -- Chapter 13. In-memory Data Compression Using ReRAMs -- Chapter 14. In-memory Data Compression Using ReRAMs -- Chapter 15.Data Analytics in Quantum Paradigm - An Introduction.

520 $aThis 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.

650 0$aBig data.$3366264

650 14$aEngineering.$3312165

650 24$aCircuits and Systems.$3401817

650 24$aProcessor Architectures.$3402650

650 24$aElectronic Circuits and Devices.$3402191

650 24$aBig Data/Analytics.$3417623

700 1 $aChattopadhyay, Anupam.$3473270

700 1 $aChang, Chip Hong.$3282209

700 1 $aYu, Hao.$3415106

710 2 $aSpringerLink (Online service)$3374217

773 0 $tSpringer eBooks

856 40$uhttps://erm.library.ntpu.edu.tw/login?url=http://dx.doi.org/10.1007/978-3-319-54840-1$z線上閱讀(Springer)

950 $aEngineering (Springer-11647)

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