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.

- Provides comprehensive coverage of neuromorphic computing based upon energy-efficient electronic devices and circuits

- Presents practical guidance and numerous examples, making it an excellent resource for researchers, engineers, and students designing energy-efficient neuromorphic computing systems

- Includes detailed coverage of emerging post-CMOS devices such as memristors and MTJs and their potential applications in energy-efficient synapses and neurons

Product Details

ISBN-13:	9780443299827
Publisher:	Elsevier Science
Publication date:	10/06/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	540
File size:	11 MB
Note:	This product may take a few minutes to download.

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 NEW2. Fundamentals of neuron dynamics and Neural Networks NEW3. Foundations, recent developments and applications of spiking neural networks (SNNs)4. Training and learning processes of SNNs5. Introduction to Neuromorphic Computing6. The Need for Energy Efficiency in Neuromorphic Computing v Review of Neuromorphic devices and Circuits7. Energy-efficient devices for Neuromorphic computing8. Novel biomimetic devices for energy efficient synapses and neurons OLD9. Analog and Digital CMOS circuits for Energy Efficient Neuromorphic Computing10. Energy-efficient Neuromorphic computing systems with emerging post-CMOS devices11. Energy Efficient Neuromorphic Computing Architectures and Processing12. Nonvolatile memory crossbar arrays for energy efficient neuromorphic computing13. Energy Efficient Neuromorphic Vision Systems14. Neuromorphic sensors and in-sensor computing15. Practical Applications of Energy-Efficient Neuromorphic Computing16. Current and future challenges of Neuromorphic Computing