Energy-Efficient Devices and Circuits for Neuromorphic Computing - Softcover

Über die Autorin bzw. den Autor

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.

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In today's world, where the demand for advanced computing systems has skyrocketed, energy efficiency has become a top priority. The development of energy-efficient neuromorphic computing systems has gained significant attention due to their ability to mimic the human brain's low-power, high-performance computing capabilities. The field of neuromorphic computing is at the forefront of research and development in emerging technologies such as artificial intelligence, robotics, and cognitive computing. Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to the field of neuromorphic computing. The book covers a wide range of topics, from the fundamentals of neuron dynamics to the latest developments in energy-efficient CMOS devices and circuits, emerging post-CMOS devices, and non-volatile memory crossbar arrays for energy-efficient neuromorphic computing. It discusses the theoretical analysis of the learning process in spiking neural networks, two-terminal neuromorphic devices, material-engineered neuromorphic devices, and novel biomimetic Si devices for energy-efficient neuromorphic computing architecture. Overall, it will be an essential resource for researchers, engineers, and students working in the fields of neuromorphic computing and energy-efficient electronics.