On-Chip AI for an Efficient Data-Driven World

  • Boris Murmann
  • Bernd Hoefflinger

Part of the The Frontiers Collection book series (FRONTCOLL)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Boris Murmann, Bernd Hoefflinger
    Pages 1-7
  3. Bernd Hoefflinger
    Pages 19-30
  4. Bernd Hoefflinger
    Pages 31-39
  5. Bernd Hoefflinger
    Pages 41-45
  6. Nobuyuki Sugii, Shiro Kamohara, Makoto Ikeda
    Pages 47-88
  7. Wim Dehaene, Roel Uytterhoeven, Clara Nieto Taladriz Moreno, Bob Vanhoof
    Pages 89-115
  8. Zvi Or-Bach
    Pages 117-125
  9. Dennis Rich, Andrew Bartolo, Carlo Gilardo, Binh Le, Haitong Li, Rebecca Park et al.
    Pages 127-151
  10. Zvi Or-Bach
    Pages 165-180
  11. Ulrich Rueckert
    Pages 181-202
  12. Raghu Prabhakar, Yaqi Zhang, Kunle Olukotun
    Pages 227-246
  13. Bernd Hoefflinger
    Pages 269-273
  14. Boris Murmann, Marian Verhelst, Yiannos Manoli
    Pages 275-292
  15. Marian Verhelst, Boris Murmann
    Pages 293-322
  16. Francesco Conti, Manuele Rusci, Luca Benini
    Pages 323-349
  17. Bernd Hoefflinger
    Pages 351-358
  18. Zhichun Lei, Xin Yu, Markus Strobel
    Pages 359-386
  19. Ulrich Rueckert
    Pages 387-403
  20. Thorsten Hehn, Alexander Bleitner, Jacob Goeppert, Daniel Hoffmann, Daniel Schillinger, Daniel A. Sanchez et al.
    Pages 405-442
  21. Dante G. Muratore, E. J. Chichilnisky
    Pages 443-465
  22. Gordon Wetzstein
    Pages 467-499
  23. Edoardo Charbon, Fabio Sebastiano, Masoud Babaie, Andrei Vladimirescu
    Pages 501-525
  24. Albert Frisch, Harry S. Barowski, Markus Brink, Peter Hans Roth
    Pages 527-548
  25. Ulrich Rueckert
    Pages 549-562
  26. Yutaka Yamada, Katsuyuki Kimura
    Pages 563-575
  27. Boris Murmann, Bernd Hoefflinger
    Pages 577-583
  28. Back Matter
    Pages 585-592

About this book


In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come. 


Nano-Electronics Artificial Intelligence CMOS Chips Energy Efficiency 3D Integration Acquisition of Information Ultra-Low-Power Processing Deep Learning Graphics Accelerators Implanted Chips HDR Vision Energy Autonomy Hardware for Robots Embedded Systems

Editors and affiliations

  • Boris Murmann
    • 1
  • Bernd Hoefflinger
    • 2
  1. 1.Department of Electrical EngineeringStanford UniversityStanfordUSA
  2. 2.SindelfingenGermany

Bibliographic information

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Chemical Manufacturing