Memristive Devices for Brain-Inspired Computing : From Materials, Devices, and Circuits to Applications Computational Memory, Deep Learning, and Spiking Neural Networks (Woodhead Publishing Series in Electronic and Optical Materials)

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Memristive Devices for Brain-Inspired Computing : From Materials, Devices, and Circuits to Applications Computational Memory, Deep Learning, and Spiking Neural Networks (Woodhead Publishing Series in Electronic and Optical Materials)

Spiga, Sabina (EDT)/ Sebastian, Abu (EDT)/ Querlioz, Damien (EDT)

ウェブストア価格 ¥55,264（本体¥50,240）
Woodhead Publishing Ltd（2020/06発売）
外貨定価 US$ 250.00
ポイント 502pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 564 p.
言語 ENG
商品コード 9780081027820
DDC分類 620.11

Full Description

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