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Hardware Accelerators in Data Centers

  • Christoforos Kachris
  • Babak Falsafi
  • Dimitrios Soudris

Table of contents

  1. Front Matter
    Pages i-ix
  2. Christoforos Kachris, Babak Falsafi, Dimitrios Soudris
    Pages 1-7
  3. Naif Tarafdar, Thomas Lin, Daniel Ly-Ma, Daniel Rozhko, Alberto Leon-Garcia, Paul Chow
    Pages 9-33
  4. Nikolaos Alachiotis, Andreas Andronikakis, Orion Papadakis, Dimitris Theodoropoulos, Dionisios Pnevmatikatos, Dimitris Syrivelis et al.
    Pages 35-56
  5. A. Scionti, O. Terzo, P. Ruiu, G. Giordanengo, S. Ciccia, G. Urlini et al.
    Pages 57-86
  6. Christoforos Kachris, Elias Koromilas, Ioannis Stamelos, Georgios Zervakis, Sotirios Xydis, Dimitrios Soudris
    Pages 87-107
  7. Ariel Oleksiak, Michal Kierzynka, Wojciech Piatek, Micha vor dem Berge, Wolfgang Christmann, Stefan Krupop et al.
    Pages 109-128
  8. Karim Djemame, Richard Kavanagh, Vasilios Kelefouras, Adrià Aguilà, Jorge Ejarque, Rosa M. Badia et al.
    Pages 129-148
  9. Kimon Karras, Orthodoxos Kipouridis, Nick Zotos, Evangelos Markakis, George Bogdos
    Pages 149-162
  10. Huanhuan Xiong, Christos Filelis-Papadopoulos, Dapeng Dong, Gabriel G. Castañé, Stefan Meyer, John P. Morrison
    Pages 163-180
  11. Tobias Kalb, Lester Kalms, Diana Göhringer, Carlota Pons, Ananya Muddukrishna, Magnus Jahre et al.
    Pages 181-197
  12. Konstantinos Georgopoulos, Iakovos Mavroidis, Luciano Lavagno, Ioannis Papaefstathiou, Konstantin Bakanov
    Pages 199-213
  13. Artemis C. Voulkidis, Terpsichori Helen Velivassaki, Theodore Zahariadis
    Pages 215-239
  14. Lev Mukhanov, Konstantinos Tovletoglou, Georgios Karakonstantis, George Papadimitriou, Athanasios Chatzidimitriou, Manolis Kaliorakis et al.
    Pages 241-271
  15. Back Matter
    Pages 273-279

About this book

Introduction

This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.


  • Provides a single-source reference to the state of the art for hardware accelerators in data centers;
  • Describes integrated frameworks for the seamless deployment of hardware accelerators;
  • Includes several use-case scenarios of hardware accelerators for typical cloud computing applications, such as machine learning, graph computation, and databases.

Keywords

Internet of Things Hardware accelerators for Machine learning Hardware accelerators for Neural Networks Hardware accelerators for Graph applications Hardware accelerators for Streaming applications

Editors and affiliations

  • Christoforos Kachris
    • 1
  • Babak Falsafi
    • 2
  • Dimitrios Soudris
    • 3
  1. 1.Microprocessors and Digital Systems LabNational Technical University of AthensAthensGreece
  2. 2.IC IINFCOM PARSAÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.National Technical University of AthensAthensGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-92792-3
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-92791-6
  • Online ISBN 978-3-319-92792-3
  • Buy this book on publisher's site