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Produktbild: Energy-Efficient Devices and Circuits for Neuromorphic Comp

Energy-Efficient Devices and Circuits for Neuromorphic Comp

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Beschreibung

Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to this field, covering topics from neuron dynamics to energy-efficient CMOS devices and circuits. The book delves into theoretical analysis of learning processes in spiking neural networks, two-terminal neuromorphic...

Beschreibung

Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to this field, covering topics from neuron dynamics to energy-efficient CMOS devices and circuits. The book delves into theoretical analysis of learning processes in spiking neural networks, two-terminal neuromorphic devices, material-engineered neuromorphic devices, and novel biomimetic Si devices. It offers insights into the latest developments in non-volatile memory crossbar arrays and emerging post-CMOS devices. Overall, it provides a comprehensive overview of energy-efficient neuromorphic computing architecture. This book is an essential resource for researchers, engineers, and students working in neuromorphic computing and energy-efficient electronics.

Portrait

Dr. Farooq Ahmad Khanday received his M.Sc. (Gold Medallist), M. Phil. and Ph.D. Degrees from the University of Kashmir. He served as Assistant Professor at University of Kashmir, Department of Electronics and Instrumentation Technology, followed by time at the Department of Higher Education J&K, the Department of Electronics and Vocational Studies, Islamia College of Science and Commerce Srinagar. He is currently associate professor in the Department of Electronics and Instrumentation Technology, University of Kashmir.

Dr Khanday's research interests include neuromorphic computing, fractional-order circuits, low-power circuit design, nano-electronics and stochastic computing. He is author or co-author of more than 150 publications, including eleven book chapters while also editing the PLOS ONE journal. He authored the book, "Nanoscale Electronic Devices and Their Applications” and edited "Neuromorphic Computing” and "Fractional-order Systems” for Elsevier. In addition, he has one patent on "Portable Microcontroller-Based Impedance Meter for Biological Tissue Analysis (563600)”. Dr Khanday was the Management Committee Observer of the COST Action CA15225 for the European Union (Fractional-order systems - analysis, synthesis and their importance for future design) and INSA Visiting Scientist Fellow 2020- 21.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2025

Herausgeber Farooq Ahma Khanday
Verlag Elsevier Science & Technology

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2025

Herausgeber Farooq Ahma Khanday
Verlag Elsevier Science & Technology
Seitenzahl

508

Maße (L/B/H)

23,5/19,1/2,6 cm

Gewicht

865 g

Sprache

Englisch

ISBN

978-0-443-29981-0

EU-Ansprechpartner

Elsevier B.V.
Radarweg 29
1043 NX Amsterdam
NL
productsafety@elsevier.com

Herstelleradresse

Elsevier Science & Technology
London Wall 125
EC2Y 5AS London
GB
tradeorders@elsevier.com

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  • Produktbild: Energy-Efficient Devices and Circuits for Neuromorphic Comp
  • 1. Biological neural systems NEW
    2. Fundamentals of neuron dynamics and Neural Networks NEW
    3. Foundations, recent developments and applications of spiking neural networks (SNNs)
    4. Training and learning processes of SNNs
    5. Introduction to Neuromorphic Computing
    6. The Need for Energy Efficiency in Neuromorphic Computing v Review of Neuromorphic devices and Circuits
    7. Energy-efficient devices for Neuromorphic computing
    8. Novel biomimetic devices for energy efficient synapses and neurons OLD
    9. Analog and Digital CMOS circuits for Energy Efficient Neuromorphic Computing
    10. Energy-efficient Neuromorphic computing systems with emerging post-CMOS devices
    11. Energy Efficient Neuromorphic Computing Architectures and Processing
    12. Nonvolatile memory crossbar arrays for energy efficient neuromorphic computing
    13. Energy Efficient Neuromorphic Vision Systems
    14. Neuromorphic sensors and in-sensor computing
    15. Practical Applications of Energy-Efficient Neuromorphic Computing
    16. Current and future challenges of Neuromorphic Computing