© 2020


On-Chip AI for an Efficient Data-Driven World

  • Boris Murmann
  • Bernd Hoefflinger
  • Presents key elements of a new epoch in nanoelectronics that follows the end of the Nanometer Roadmap

  • A timely compendium that will inspire and shape the future of nanoelectronics

  • Explores the next generation of intelligent and energy-efficient chip-systems related to health and information technology

  • Broadens perspectives of the existing chip-savvy audience while appealing to new readers in system design


Part of the The Frontiers Collection book series (FRONTCOLL)

Table of contents

  1. Bernd Hoefflinger
    Pages 351-358
  2. Zhichun Lei, Xin Yu, Markus Strobel
    Pages 359-386
  3. Ulrich Rueckert
    Pages 387-403
  4. Thorsten Hehn, Alexander Bleitner, Jacob Goeppert, Daniel Hoffmann, Daniel Schillinger, Daniel A. Sanchez et al.
    Pages 405-442
  5. Dante G. Muratore, E. J. Chichilnisky
    Pages 443-465
  6. Gordon Wetzstein
    Pages 467-499
  7. Edoardo Charbon, Fabio Sebastiano, Masoud Babaie, Andrei Vladimirescu
    Pages 501-525
  8. Albert Frisch, Harry S. Barowski, Markus Brink, Peter Hans Roth
    Pages 527-548
  9. Ulrich Rueckert
    Pages 549-562
  10. Yutaka Yamada, Katsuyuki Kimura
    Pages 563-575
  11. Boris Murmann, Bernd Hoefflinger
    Pages 577-583
  12. 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

About the editors

Bernd Hoefflinger became an Assistant Professor at Cornell University, Ithaca, NY, USA, after completing his Ph.D. at the Technical University of Munich, Germany. He was a co-founder of the MOS Division of Siemens in Munich, and founded the Electrical Engineering Department of the University of Dortmund, Germany, which houses the first Ion-Implanted BiCMOS production line. After serving as Head of the Electrical Engineering Departments at the University of Minnesota and then at Purdue University in Indiana, he established the Institute of Microelectronics Stuttgart, Germany, as the first ISO 9000-certified research and manufacturing facility – a leader in ASICs, HDR vision, and e-beam-driven nanotechnology.

Boris Murmann received his Ph.D. degree from the University of California, Berkeley, in 2003, and serves as a Professor of Electrical Engineering at Stanford University. His research interests are in mixed-signal integrated circuit design, with a focus on sensor interfaces, data converters, and custom circuits for embedded machine learning. He has served as an Associate Editor of the IEEE Journal of Solid-State Circuits, an AdCom member and a Distinguished Lecturer of the IEEE Solid-State Circuits Society, as well as the Data Converter Subcommittee Chair and the Technical Program Chair of the IEEE International Solid-State Circuits Conference (ISSCC). He is a Fellow of the IEEE.

Bibliographic information

Industry Sectors
Chemical Manufacturing


​“Less than 10 years ago, the Springer book Chips 2020 laid out the path to the future of nano-electronics in an era where energy-efficient and sustainable integrated circuits should become a new style of living. Now, Bernd Hoefflinger and Boris Murmann have re-emerged onto the scene with NANO-CHIPS 2030, highlighting novel solutions for energy-aware micro- and nano-electronics. These solutions come as a sum of actions at all levels: from manufacturing solutions that enable new ‘More than Moore’ silicon technologies to entirely new system and design opportunities. The two major paradigm changes in data processing – artificial intelligence towards virtual reality and quantum computing – are largely debated as THE solutions for the 2030 world of sustainable electronics. In thirty chapters, the editors, along with a stellar line-up of senior experts from throughout industry and academia, outline their vision for the nano-electronics roadmap of the future. A must read, enlightening and visionary!” (Dr. Andreia Cathelin, Technology R&D Fellow, STMicroelectronics)

“NANO-CHIPS 2030 provides an up-to-date and exhaustive guide on integrated circuits, systems and applications with a focus on artificial intelligence, synthesizing the most relevant ground-breaking research of the past decade. It serves as an excellent resource for researchers to broaden their horizons and identify parallel efforts in adjacent fields. Electronic designers looking to exploit full-stack optimization will certainly get their money’s worth. Through topics like extreme energy efficiency, 3D integration and brain-inspired design, the book discusses solutions to the end of traditional CMOS scaling. Neuromorphic engineering clearly surfaces as the way forward: AR/VR and human-machine interfacing shall leverage perception science, neuro-inspired architectures and AI algorithms to the extreme. All these and more are presented in an approachable manner, rewarding the reader with the ability to deepen their expertise, connect the dots and disentangle the mysteries of on-chip AI.” (Dr. Hans Reyserhove, Research Scientist in Intelligent Vision Systems, Facebook Reality Labs Research)