Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks

by Sabina Spiga, Abu Sebastian, Damien Querlioz, Bipin Rajendran

Overview

Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications—Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning.

This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists.

Product Details

ISBN-13:	9780081027820
Publisher:	Elsevier Science
Publication date:	06/12/2020
Series:	Woodhead Publishing Series in Electronic and Optical Materials
Pages:	564
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Sabina Spiga received the Degree in Physics from the Università di Bologna in 1995 and the PhD in Material Science in 2002 from Università di Milano. She is staff researcher at CNR-IMM–Unit of Agrate Brianza (Italy) since 2004, and she is currently leading a research team developing oxide-based resistive switching non-volatile memories and memristive devices for neuromorphic systems. She is currently principal Investigator for CNR of the project European project-Horizo2020 NeuRAM3-NEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies; and since 2014 she is also member of the Management Committee for Italy of the COST Action ICT 1401-“Memristors-Devices, Models, Circuits, Systems and Applications”. S. Spiga is co-author of more than 100 publications on peer reviewed journals and proceedings. She co-organized several symposia and workshops and national and international level, and in 2013/2014 she participated to the IEDM Memory Technology subcommittee.

IBM Research – Zurich, Switzerland
Abu Sebastian received a B. E. (Hons.) degree in Electrical and Electronics Engineering from BITS Pilani, India, in 1998 and M.S. and Ph.D. degrees in Electrical Engineering (minor in Mathematics) from Iowa State University in 1999 and 2004, respectively. Since 2006, he is a Research Staff Member at IBM Research - Zurich in Rüschlikon, Switzerland.
He was a contributor to several key projects in the space of storage and memory technologies such as probe-based data storage, phase-change memory and carbon-based memory. Most recently, he is actively researching the area of non-von Neumann computing with the intent of connecting the technological elements with applications such as cognitive computing. He has published over 140 articles in journals and conference proceedings. He also holds over 30 granted patents. He is a co-recipient of the 2009 IEEE Control Systems Technology Award. In 2015 he was awarded the European Research Council (ERC) consolidator grant and in 2016 he was named an IBM Master Inventor.
He served on the editorial board of the journal, Mechatronics from 2008 till 2015. He also served on the memory technologies committee of the IEDM from 2015-2016.

Damien Querlioz is a CNRS researcher at the Centre for Nanoscience and Nanotechnology of Université Paris-Sud, Orsay. He received his predoctoral education at Ecole Normale Supérieure, Paris, his PhD at Université Paris-Sud in 2008, and was a postdoctoral scholar at Stanford University and CEA. He focuses on novel usages of emerging non-volatile memory, in particular relying on inspirations from biology and machine learning. He coordinates the INTEGNANO interdisciplinary research group. In 2016, he was the recipient of an ERC Starting Grant to develop the concept of natively intelligent memory.

Bipin Rajendran received a B.Tech degree from I.I.T. Kharagpur, in 2000, and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, in 2003 and 2006, respectively. He was a Master Inventor and Research Staff Member at IBM T. J. Watson Research Center in New York during 2006-'12 and a faculty member in the Electrical Engineering Department at I.I.T. Bombay during 2012-'15. His research focuses on building algorithms, devices, and systems for brain-inspired computing. He has authored over 60 papers in peer-reviewed journals and conferences and has been issued 55 U.S. patents. His work has been recognized by Research Division Award and Technical Accomplishment Awards by IBM. He is a senior Member of IEEE and has served on the technical program committee of several IEEE conferences such as DRC, NVMW etc. His research in various aspects of Neuromorphic Computing has been funded by National Science Foundation, Semiconductor Research Corporation, IBM, Intel, and CISCO. He is currently an Associate Professor of Electrical and Computer Engineering at New Jersey Institute of Technology,USA.

Table of Contents

Part I Memristive devices for brain–inspired computing 1. Role of resistive memory devices in brain-inspired computing 2. Resistive switching memories 3. Phase change memories 4. Magnetic and Ferroelectric memories 5. Selectors for resistive memory devices

Part II Computational Memory 6. Memristive devices as computational memory 7. Logical operations 8. Hyperdimensional Computing Nanosystem: In-memory Computing using Monolithic 3D Integration of RRAM and CNFET 9. Matrix vector multiplications using memristive devices and applications thereof 10. Computing with device dynamics 11. Exploiting stochasticity for computing

Part III Deep learning 12. Memristive devices for deep learning applications 13. PCM based co-processors for deep learning  14. RRAM based co-processors for deep learning

Part IV Spiking neural networks 15. Memristive devices for spiking neural networks 16. Neuronal realizations based on memristive devices 17. Synaptic realizations based on memristive devices 18. Neuromorphic co-processors and experimental demonstrations 19. Recent theoretical developments and applications of spiking neural networks

What People are Saying About This

From the Publisher

Provides the fundamental principles of emerging interdisciplinary research on material and device engineering for optimizing resistive memory devices