Primer to Neuromorphic Computing
Coordonnateurs : Garg Harish, Moy Chatterjee Jyotir, Sujatha R, Modi Shatrughan
Primer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientific concepts for the research topic of neuromorphic computing, such as neurons as computational units, artificial intelligence, machine learning, and neuromorphic models. It also discusses the fundamental design method and organization of neuromorphic architecture. Hardware for neuromorphic systems can be developed by exploiting the magnetic properties of different materials. These systems are more energy efficient and enable faster computation . Magnetic tunnel junctions and magnetic textures can be employed to act as neurons and synapses. Neuromorphic systems have general intelligence like humans as they can apply knowledge gained in one domain to other domains.
2. Integrating neuromorphic components
3. Evolution and goals of neuromorphic systems
4. Neuromorphic systems vs artificial neural network
5. Hardware based on physical properties for neuromorphic computers
6. Contrasting neuromorphic system from other systems
7. Neuromorphic systems for Autonomous vehicles
8. Neuromorphic systems for smart home devices
9. Neuromorphic systems for natural language understanding
10. Neuromorphic systems for data analytics
11. Neuromorphic systems for process optimization
12. Neuromorphic systems for real-time image processing
13. Technological limitations in building and employing neuromorphic systems
14. Analysis on physical, electronic, and technological limitations in building neuromorphic systems
Dr. Garg's research interests include Computational Intelligence, Multi-criteria decision making, Evolutionary algorithms, Reliability analysis, Expert systems, and decision support systems, Computing with words, and Soft Computing. He has authored more than 400 papers (over 350 are SCI) published in refereed International Journals including IEEE Transactions, Elsevier, Springer, etc. He has also authored seven book chapters. Also, he edited 8 books from Elsevier, Springer, and other publishers. His Google citations are over 17650 with an H-index of- 75. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, CAAI Transactions on Intelligence Technology, etc.
Dr. Garg also shared his knowledge (Teaching as well as Research) with the whole world through their YouTube channel https://www.youtube.com/c/DrHarishGarg. For more details about him, kindly follow his webpage https://sites.google.com/site/harishg58iitr/home
Jyotir Moy Chatterjee received M. Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha in 2016,
- Discusses potential neuromorphic applications in computing
- Presents current trends and models in neuromorphic computing and neural network hardware architectures
- Shows the development of novel devices and hardware to enable neuromorphic computing
- Offers information about computation and learning principles for neuromorphic systems
- Provides information about Neuromorphic implementations of neurobiological learning algorithms
- Discusses biologically inspired neuromorphic systems and devices (including adaptive bio interfacing and hybrid systems consisting of living matter and synthetic matter)
Date de parution : 11-2024
Ouvrage de 370 p.
