Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Springer, India, Private Ltd (Verlag)
978-81-322-3890-4 (ISBN)
Dr. Manan Suri (Member, IEEE) is an Assistant Professor with the Department of Electrical Engineering, Indian Institute of Technology – Delhi (IIT-Delhi). He was born in India in 1987. He received his PhD in Nanoelectronics and Nanotechnology from Institut Polytechnique de Grenoble (INPG), France in 2013. He obtained his M.Eng. (2010) and B.S (2009) in Electrical & Computer Engineering from Cornell University, USA. Prior to joining IIT-Delhi, he worked as a Senior Scientist with NXP Semiconductors, Belgium. His research interests include Non-Volatile Memory Technology, Unconventional Computing (Machine-Learning/Neuromorphic), and Semiconductor Devices. He holds several granted and filed US, European and Indian patents. He has authored book chapters and more than 30 papers in reputed international conferences and journals. He serves as committee member and reviewer for IEEE journals/conferences. He is a recipient of several prestigious national and international honors such as the IEI Young Engineers Award, Kusuma Outstanding Young Faculty Fellowship, and Laureat du Prix (NSF-France).
Phase Change Memory for Neuromorphics .- Filamentary resistive memory for Neuromorphics.- Metal oxide based memory for Neuromorphics.- Nano Organic Transistors for Neuromorphics.- Neuromorphic System design.- Neuromorphic System and algorithms optimization.- Memristor Technology for Neuromorphics.- PCMO based devices for Neuromorphics.- Resistive Memory for Neuromorphics.- Overall Perspective on Neuromorphic Hardware.
Erscheinungsdatum | 18.08.2018 |
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Reihe/Serie | Cognitive Systems Monographs ; 31 |
Zusatzinfo | 123 Illustrations, black and white; XIII, 210 p. 123 illus. |
Verlagsort | New Delhi |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Technik ► Elektrotechnik / Energietechnik | |
Schlagworte | Low-power Cognitive Hardware • memristor • Neuromorphic Hardware • Resistive Memory Technology • Supervised and Unsupervised Learning |
ISBN-10 | 81-322-3890-7 / 8132238907 |
ISBN-13 | 978-81-322-3890-4 / 9788132238904 |
Zustand | Neuware |
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