rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Emerging Technology and Architecture for Big-data Analytics
  name:
    name part: Chattopadhyay, Anupam.
    role:
      role term: editor.
  name:
    name part: Chang, Chip Hong.
    role:
      role term: editor.
  name:
    name part: Yu, Hao.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 330 p. 162 illus., 98 illus. in color. online resource.
  abstract: 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.
  table of contents: 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.
  note: edited by Anupam Chattopadhyay, Chip Hong Chang, Hao Yu.
  subject:
    topic: Engineering
  subject:
    topic: Big data
  subject:
    topic: Microprocessors
  subject:
    topic: Electronic circuits
  subject:
    topic: Engineering
  subject:
    topic: Circuits and Systems
  subject:
    topic: Processor Architectures
  subject:
    topic: Electronic Circuits and Devices
  subject:
    topic: Big Data/Analytics
  classification: TK7888.4
  classification: 621.3815
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319548401
  identifier: http://dx.doi.org/10.1007/978-3-319-54840-1
  record info:
    record creation date: 170420
    record change date: 20170420102749.0
    record identifier: 978-3-319-54840-1