Recent Advances In Neuromorphic Computing
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Recent Advances in Neuromorphic Computing
Artificial Intelligence (AI) is a transformative technology that reshapes our daily lives. Machine Learning (ML), the engine of such a revolution, empowers computers to learn from data, driving innovation in areas such as medicine, robotics, and smart cities through edge applications. These applications bring AI processing closer to the data source, enabling real-time insights and decisions. This evolution is fueled by advancements in hardware and architecture: (1) neuromorphic computing promises unparalleled efficiency; (2) in-memory computing eliminates data access bottlenecks, while emerging memory materials offer denser, faster, and more energy-efficient storage. Looking ahead, AI promises even more profound changes. For instance, explainable AI will make decision-making more transparent, and truly autonomous systems will adapt to unforeseen circumstances. Last but not least, the convergence of AI with quantum computing could unlock entirely new possibilities. This journey showcases a deep understanding of both the theoretical foundations and practical applications of AI. It also demands careful consideration of ethical implications and a commitment to responsible development, ensuring that AI benefits all of humanity.
Neuromorphic Devices for Brain-inspired Computing
Explore the cutting-edge of neuromorphic technologies with applications in Artificial Intelligence In Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics, a team of expert engineers delivers a comprehensive discussion of all aspects of neuromorphic electronics designed to assist researchers and professionals to understand and apply all manner of brain-inspired computing and perception technologies. The book covers both memristic and neuromorphic devices, including spintronic, multi-terminal, and neuromorphic perceptual applications. Summarizing recent progress made in five distinct configurations of brain-inspired computing, the authors explore this promising technology’s potential applications in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. The book also includes: A thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms Comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviors Practical discussions of neuromorphic devices based on chalcogenide and organic materials In-depth examinations of neuromorphic computing and perceptual systems with emerging devices Perfect for materials scientists, biochemists, and electronics engineers, Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics will also earn a place in the libraries of neurochemists, neurobiologists, and neurophysiologists.
Advances in Memristor Neural Networks - Modeling and Applications
Nowadays, scientific research deals with alternative solutions for creating non-traditional computing systems, such as neural network architectures where the stochastic nature and live dynamics of memristive models play a key role. The features of memristors make it possible to direct processing and analysis of both biosystems and systems driven by artificial intelligence, as well as develop plausible physical models of spiking neural networks with self-organization. This book deals with advanced applications illustrating these concepts, and delivers an important contribution for the achievement of the next generation of intelligent hybrid biostructures. Different modeling and simulation tools can deliver an alternative to funding the theoretical approach as well as practical implementation of memristive systems.