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

Memristive Devices for Brain-Inspired Computing - 1st Edition - ISBN: 9780081027820, 9780081027875Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications – Computational Memory, Deep Learning, and Spiking Neural Networks
By 作者: Sabina Spiga
Pub Date: 2020
ISBN: 9780081027820
Pages 页数: 564
Language 语言: English
Format: PDF
Size: 16 Mb
The Book Description robot was collected from Amazon and arranged by Finelybook

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. 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


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