Energy-Efficient Devices and Circuits for Neuromorphic Computing

by Farooq Ahmad Khanday PhD (Editor)

Overview

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.

Product Details

ISBN-13:	9780443299810
Publisher:	Elsevier Science
Publication date:	10/06/2025
Pages:	540
Product dimensions:	7.50(w) x 9.25(h) x 0.00(d)

About the Author

Farooq Ahmad Khanday (M’15, SM’19) received B.Sc., M.Sc., M. Phil. and Ph.D. Degrees from the University of Kashmir in 2001, 2004 2010 and 2013 respectively. From June 2005 to Jan. 2009, he served as Assistant Professor on a contractual basis at the University of Kashmir, Department of Electronics and Instrumentation Technology. In 2009, Dr. Khanday joined the Department of Higher Education J&K and Department of Electronics and Vocational Studies, Islamia College of Science and Commerce Srinagar, as Assistant Professor. In May 2010, he joined as Assistant Professor in the Department of Electronics and Instrumentation Technology, University of Kashmir. His research interests include Fractional-order Circuits, Nano-Electronics, Low-voltage analog integrated circuit design, Hardware Neural Network, Quantum Computing, Stochastic Computing, and Biomedical Circuit Design. Dr. Khanday is the author or co-author of more than 85 publications in peer-reviewed indexed International and National journals/conferences of repute and three book chapters. A book of Dr. Khanday on “Nanoscale Electronic Devices and their Applications” is to appear in July 2020. Dr. Khanday is the Management Committee (MC) Observer of the COST Action CA15225 (Fractional-order systems - analysis, synthesis, and their importance for future design) of the European Union. He is the senior member of IEEE and a member of several professional societies. Dr. Khanday is serving as a reviewer for many reputed International and National scientific journals in Electronics. He has successfully guided many Ph.D., M. Phil scholars, and M. Tech thesis. Dr. Khanday also has completed/ongoing funded research projects to his credit and has established laboratories with state of the art facilities for pursuing research in the field of IC design, Nanoelectronics, fractional-order systems, etc.

Table of Contents

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

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From the Publisher

Covers the field of neuromorphic computing, along with practical advice on designing energy-efficient CMOS devices and circuits