Memristive Computing

Erscheinungsjahr 2025
ISBN: 978-981-960331-2
Verlag: Springer Nature Singapore

Buch, Englisch, 376 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 821 g

This book delves into a wide array of topics, ranging from memristor and its emulator to chaotic circuits based on memristor, memristor-based en/decryption systems, filter design based on memristive family, memristive filter for signal processing, memristor network-based swarm intelligence, dynamic analysis of memristive neural networks, and the application of memristor-based neural networks. It provides a comprehensive and systematic exploration of how memristors empower and drive cutting-edge research in neuromorphic computing and artificial intelligence. This book encourages fostering interdisciplinary information literacy and cultivating cross-disciplinary computational thinking. This book plays a pivotal role in embracing and advancing the development of neuromorphic computing. Through profound foundational theories and academic analysis methods, this book guides artificial intelligence graduate students and engineering professionals in constructing a comprehensive knowledge and technological framework for memristor research.

Zielgruppe

Research

Autoren/Hrsg.

Yu, Yongbin

Wang, Xiangxiang

Mazumder, Pinaki

Feng, Xiao

Shen, Jiarun

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

Chua Corsage Memristor.- Memristor Model and its Emulator.- Chaotic Circuits based on Memristor.- Memristor-based En/decryption System.- Filter Design based on Memristive Family.- Memristive Filter for Signal Processing.- Memristor network-based Swarm Intelligence.- Memristor-based Neural Network.-Memristive Neural Network Stability Analysis.- Memristive Neural Network Synchronization Analysis.- Mismatched Memristive Neural Network Synchronization Analysis on Time Scales.

Über Autor(innen)

Yongbin Yu was born in Sichuan. He received the Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2008. He visited the University of Michigan at Ann Arbor, Ann Arbor, MI, USA, in 2013, and the University of California at Santa Barbara, Santa Barbara, CA, USA, in 2016. He worked as the guest deputy director with the Department of Big Data Industry, Sichuan Provincial Economic and Information Commission, in 2018. He is currently an associate professor with the School of Information and Software Engineering, UESTC. His research interests include memristor-based neural network, swarm intelligence, natural language processing, and big data. Dr. Yu won the First Prize of Science and Technology Award of Tibet Autonomous Region in 2018.

Xiangxiang Wang was born in Henan Province. He received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, in 2023. From June 2021 to June 2022, he was a joint Ph.D. student with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, sponsored by the Academic Exchange Special Fund in the University of Electronic Science and Technology of China for Overseas Training. From September 2023, he has been a lecturer in the School of Information and Software Engineering, University of Electronic Science and Technology of China. He has published over 30 academic papers and is a member of IEEE, the China Computer Federation (CCF), and the Chinese Association of Automation. He has served as a Technical Program Committee member for international conferences like DMEIS 2024. Additionally, he reviews for several prominent international journals and conferences, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Fuzzy Systems, the International Journal of Systems Science, and key conferences such as ICPMS, MICCAI, and IFAC World Congress. His research interests focus on memristive neural networks, complex neural networks, impulsive control, and synchronization analysis.

Xiao Feng was born in Sichuan Province, China. He received the M.E. degree in integrated design engineering from Keio University, Yokohama, Japan, in 2020. He is currently pursuing the Ph.D. degree in the School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include evolutionary algorithm, neural network, NAS, and control system.

Jiarun Shen was born in Shandong, China, in 2000. He received the B.S. degree in Computer Science and Technology from Southwest University, in 2022. He is currently pursuing the Ph.D. degree in software engineering at University of Electronic Science and Technology of China. His current research interests include swarm intelligence algorithm, memristor, and fuzzy system.

Nyima Tashi received the B.S. degree from East China Normal University, Shanghai, China, in 1988, and the Ph.D. degree from Sichuan University, Chengdu, China, in 2009. He was elected as an academician of the Chinese Academy of Engineering in 2023. His research field is language intelligence and natural language processing. He has opened up new fields of Chinese Tibetan information technology research. He has made a series of systematic achievements in the fields of computer Tibetan coding, computational linguistics theory and methods, digitalization of Tibetan ancient literature, Tibetan natural language processing, computer operating system, and mobile operating system Tibetan processing.

Pinaki Mazumder received the Ph.D. degree in electrical and computer engineering from the University of Illinois, Urbana-Champaign, in 1988. Currently, he is a professor of electrical engineering and computer science at the University of Michigan where he has been teaching for the past 25 years. He spent three years with the National Science Foundation serving as the lead program director. He had worked for six years in industrial R&D laboratories on VLSI chip design. He has published more than 350 technical papers and 8 books on various aspects of VLSI technology and systems. Prof. Mazumder was a recipient of Digital’s Incentives for Excellence Award, BF Goodrich National Collegiate Invention Award, and DARPA Research Excellence Award. He is an AAAS fellow (2007) and an IEEE fellow (1999) for his distinguished contributions to the field of VLSI.