Energy-Efficient Devices and Circuits for Neuromorphic Computing
- 1st Edition - October 29, 2025
- Latest edition
- Editor: Farooq Ahmad Khanday
- Language: English
Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to this field, covering topics from neuron dynamics to energy-efficient CMOS device… Read more
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Description
Description
Key features
Key features
- 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
Readership
Readership
Engineers and professionals in the semiconductor industry who are involved in the design and development of energy-efficient CMOS devices and circuits for neuromorphic computing.
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: October 29, 2025
- Language: English
About the editor
About the editor
FK
Farooq Ahmad Khanday
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.