Energy-Efficient Devices and Circuits for Neuromorphic Computing

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.

• 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