MemComputing: Fundamentals and Applications

by Massimiliano Di Ventra

Overview

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.

Product Details

ISBN-13:	9780192845320
Publisher:	Oxford University Press
Publication date:	05/21/2022
Pages:	384
Product dimensions:	9.83(w) x 7.63(h) x 0.80(d)

About the Author

Massimiliano Di Ventra, Professor of Physics, Department of Physics, University of California, San Diego

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.

Table of Contents

Preface1. The physics of computing and computing with physics2. Computing: The Turing way3. Dynamical systems picture of computing4. Memory, memelements, active elements, and memprocessors5. Universal MemComputing machine6. Digital MemComputing machines7. Self-organizing gates and circuits8. Numerical simulations of DMMs9. Application to combinatorial optimization problems10. Application to machine learning and quantum mechanics11. Topological features of DMMs12. Approach to equilibrium and the ordered state of DMMs13. Epilogue and future work