Freshly Printed - allow 10 days lead
Couldn't load pickup availability
Memristive Devices for Brain-Inspired Computing
From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
Provides the fundamental principles of emerging interdisciplinary research on material and device engineering for optimizing resistive memory devices
Sabina Spiga (Edited by), Abu Sebastian (Edited by), Damien Querlioz (Edited by), Bipin Rajendran (Edited by)
9780081027820
Paperback / softback, published 12 June 2020
564 pages, 285 illustrations (60 in full color)
22.9 x 15.1 x 3.5 cm, 0.91 kg
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.
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
Subject Areas: Electronics & communications engineering [TJ], Materials science [TGM]
