Name: Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and. Series in Electronic and Optical Materials) (en Inglés)
Price: 241780.00 CLP
Availability: InStock
ISBN: 9780081027820

portada Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and. Series in Electronic and Optical Materials) (en Inglés)

Formato

Libro Físico

Editorial

Woodhead Publishing

Año

2020

Idioma

Inglés

N° páginas

564

Encuadernación

Tapa Blanda

ISBN13

9780081027820

N° edición

Categorías

Ingeniería electrónica

Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and. Series in Electronic and Optical Materials) (en Inglés)

Libro Físico

$ 241.780

$ 402.970

Ahorras: $ 161.190

40% descuento

Envío normal

Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and. Series in Electronic and Optical Materials) (en Inglés)"

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.