Memcomputing

Fundamentals and Applications
Erscheinungsjahr 2022
ISBN: 978-0-19-284532-0
Verlag: Oxford University Press

Buch, Englisch, 384 Seiten, Print PDF, Format (B × H): 194 mm x 250 mm, Gewicht: 993 g

MemComputing is a new computing paradigm that employs time non-locality (memory) to both process and store information. This book, written by the originator of this paradigm, explains the main ideas behind MemComputing, explores its theoretical foundations, and shows its applicability to a wide variety of combinatorial optimization problems, machine learning, and quantum mechanics. The book is ideal for graduate students in Physics, Computer Science, Electrical Engineering, and Mathematics, as well as researchers in both academia and industry interested in unconventional computing. The author relies on extensive margin notes, important remarks, and many illustrations to better explain the main concepts and clarify jargon, making the book as self-contained as possible. The reader will be guided from the basic notions to the more advanced ones with an always clear and engaging writing style. Along the way, the reader will appreciate the advantages of this computing paradigm and the major differences that set it apart from the prevailing Turing model of computation, and even quantum computing.

Autoren/Hrsg.

Di Ventra, Massimiliano

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Physik Quantenphysik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
• Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur

Inhaltsverzeichnis

- Preface

- 1: The physics of computing and computing with physics

- 2: Computing: The Turing way

- 3: Dynamical systems picture of computing

- 4: Memory, memelements, active elements, and memprocessors

- 5: Universal MemComputing machine

- 6: Digital MemComputing machines

- 7: Self-organizing gates and circuits

- 8: Numerical simulations of DMMs

- 9: Application to combinatorial optimization problems

- 10: Application to machine learning and quantum mechanics

- 11: Topological features of DMMs

- 12: Approach to equilibrium and the ordered state of DMMs

- 13: Epilogue and future work

Über Autor(innen)

Massimiliano Di Ventra obtained his undergraduate degree in Physics summa cum laude from the University of Trieste (Italy) in 1991 and did his PhD studies at the Swiss Federal Institute of Technology in Lausanne 1993-1997. He is now professor of Physics at the University of California, San Diego. Di Ventra's research interests are in condensed-matter theory and unconventional computing. He has been invited to deliver more than 300 talks worldwide on these topics. He has published more than 200 papers in refereed journals and 3 textbooks, and has 4 granted patents. He is a fellow of the American Physical Society and the Institute of Physics, and a foreign member of Academia Europaea. In 2018 he was named Highly Cited Researcher by Clarivate Analytics and he is the recipient of the 2020 Feynman Prize for Theory in Nanotechnology.