Memristive Devices for Brain-Inspired Computing

From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
Herausgegeben:Spiga, Sabina; Sebastian, Abu; Querlioz, Damien; Rajendran, Bipin

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Woodhead Publishing Series in Electronic and Optical Materials
• Verlag: Elsevier Science & Technology / Woodhead Publishing
• Artikelnr. des Verlages: C2017-0-04786-9
• Englisch
• Abmessung: 26mm x 152mm x 229mm
• Gewicht: 910g
• ISBN-13: 9780081027820
• Artikelnr.: 56925721

Autorenporträt

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.

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.

Inhaltsangabe

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