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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

Specificaties
Paperback, blz. | Engels
Elsevier Science | e druk, 2020
ISBN13: 9780081027820
Rubricering
Elsevier Science e druk, 2020 9780081027820
€ 265,00
Levertijd ongeveer 8 werkdagen

Samenvatting

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.

Specificaties

ISBN13:9780081027820
Taal:Engels
Bindwijze:Paperback

Inhoudsopgave

<p>Part I Memristive devices for brain–inspired computing<br>1. Role of resistive memory devices in brain-inspired computing <br>2. Resistive switching memories <br>3. Phase change memories<br>4. Magnetic and Ferroelectric memories<br>5. Selectors for resistive memory devices</p> <p>Part II Computational Memory<br>6. Memristive devices as computational memory <br>7. Logical operations<br>8. Hyperdimensional Computing Nanosystem: In-memory Computing using Monolithic 3D Integration of RRAM and CNFET<br>9. Matrix vector multiplications using memristive devices and applications thereof<br>10. Computing with device dynamics <br>11. Exploiting stochasticity for computing</p> <p>Part III Deep learning<br>12. Memristive devices for deep learning applications <br>13. PCM based co-processors for deep learning  <br>14. RRAM based co-processors for deep learning</p> <p>Part IV Spiking neural networks<br>15. Memristive devices for spiking neural networks <br>16. Neuronal realizations based on memristive devices<br>17. Synaptic realizations based on memristive devices <br>18. Neuromorphic co-processors and experimental demonstrations <br>19. Recent theoretical developments and applications of spiking neural networks</p>
€ 265,00
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        Memristive Devices for Brain-Inspired Computing