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

Memristive Devices in Brain-Inspired Computing reviews the current directions in material and devices engineering for optimizing resistive memory devices beyond storage applications and towards brain-inspired computing. The book provides readers with an understanding of four key concepts: 1. Materials and device aspects with a view of current materials systems and their remaining barriers, and how the different and more mature technologies can be suitable for various architecture and/or applications. 2. Algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts and how these are enabled by memristive technologies. 3. Circuits and architectures implementing those algorithms based on memristive technologies, also co-integrated with CMOS when relevant.4. Target applications including brain-inspired computing, computational memory, and deep learning.Memristive Devices in Brain-Inspired Computing is suitable for an interdisciplinary audience including materials scientists, physicists, electrical engineers, and computer scientists. Provides readers an overview of four key concepts in this emerging research topic including materials and device aspects, algorithmic aspects, circuits and architectures, and target applicationsCovers a broad range of applications such as brain-inspired computing, computational memory, deep learning and spiking neural networksIncludes perspectives from a wide range of discipline: materials science, electrical engineering and computing, providing a unique interdisciplinary look at the field INDICE: 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 9. Matrix vector multiplications 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

• ISBN: 978-0-08-102782-0
• Editorial: Woodhead Publishing
• Encuadernacion: Rústica
• Páginas: 575
• Fecha Publicación: 01/12/2019
• Nº Volúmenes: 1
• Idioma: Inglés