Emerging technology and architecture for big-data analytics
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 eme...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Cham :
Springer,
2017
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| Žánr/forma: | sborníky elektronické knihy |
| ISBN: | 978-3-319-54840-1 978-3-319-54839-5 |
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| On-line přístup: | Plný text |
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| 245 | 0 | 0 | |a Emerging technology and architecture for big-data analytics / |c Anupam Chattopadhyay, Chip Hong Chang, Hao Yu, edited |
| 264 | 1 | |a Cham : |b Springer, |c [2017] | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online zdroj (XI, 330 stran) : |b 162 ilustrací, 98 barevných ilustrací | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Obsahuje bibliografie | ||
| 505 | 0 | |a Part 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 | |a 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. | ||
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| 700 | 1 | |a Chang, Chip-Hong |7 xx0039167 |4 edt | |
| 700 | 1 | |a Yu, Hao, |d 1976- |7 ntk20191035840 |4 edt |0 https://orcid.org/0000-0001-8747-3203 | |
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| 776 | 0 | 8 | |i Tištěné vydání: |t Emerging technology and architecture for big-data analytics |z 978-3-319-54839-5 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-319-54840-1 |y Plný text |
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| 950 | |a Springer |b Engineering 2017 | ||