TY - BOOK
T1 - Memristive Devices for Brain-Inspired Computing
T2 - From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
AU - Spiga, Sabina
AU - Sebastian, Abu
AU - Querlioz, Damien
AU - Rajendran, Bipin
N1 - Publisher Copyright:
© 2020 Elsevier Ltd.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85124933563&partnerID=8YFLogxK
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U2 - 10.1016/B978-0-08-102782-0.00020-4
DO - 10.1016/B978-0-08-102782-0.00020-4
M3 - Book
AN - SCOPUS:85124933563
BT - Memristive Devices for Brain-Inspired Computing
PB - Elsevier
ER -