Recent Advances In Neuromorphic Computing
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Recent Advances In Neuromorphic Computing
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2025-07-02
Recent Advances In Neuromorphic Computing written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-02 with Computers categories.
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
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Author : Qing Wan
language : en
Publisher: John Wiley & Sons
Release Date : 2022-05-16
Neuromorphic Devices For Brain Inspired Computing written by Qing Wan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-16 with Technology & Engineering categories.
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
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Author : Calin Ciufudean
language : en
Publisher:
Release Date : 2018
Advances In Memristor Neural Networks Modeling And Applications written by Calin Ciufudean and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Mathematics categories.
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.
Real World Applications Of Genetic Algorithms
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Author : Olympia Roeva
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-03-07
Real World Applications Of Genetic Algorithms written by Olympia Roeva and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-07 with Computers categories.
The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi-objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The book examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc.
Neuromorphic Computing
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2023-11-15
Neuromorphic Computing written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-15 with Computers categories.
Dive into the cutting-edge world of Neuromorphic Computing, a groundbreaking volume that unravels the secrets of brain-inspired computational paradigms. Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of neuromorphic systems, empowering both experts and newcomers to embrace the limitless potential of brain-inspired computing. Discover the fundamental principles that underpin neural computation as we journey through the origins of neuromorphic architectures, meticulously crafted to mimic the brain’s intricate neural networks. Unlock the true essence of learning mechanisms – unsupervised, supervised, and reinforcement learning – and witness how these innovations are shaping the future of artificial intelligence.
Bio Inspired Neurocomputing
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Author : Akash Kumar Bhoi
language : en
Publisher: Springer Nature
Release Date : 2020-07-21
Bio Inspired Neurocomputing written by Akash Kumar Bhoi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-21 with Technology & Engineering categories.
This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.
Recent Advances In Next Generation Data Science
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Author : Henry Han
language : en
Publisher: Springer Nature
Release Date : 2024-08-22
Recent Advances In Next Generation Data Science written by Henry Han and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-22 with Computers categories.
This book constitutes the refereed proceedings of the Third Southwest Data Science Conference, on Recent advances in next-generation data science, SDSC 2024, held in Waco, TX, USA, in March 22, 2024. The 15 full papers presented were carefully reviewed and selected from 59 submissions. These papers focus on AI security in next-generation data science and address a range of challenges, from protecting sensitive data to mitigating adversarial threats.
Photo Electroactive Non Volatile Memories For Data Storage And Neuromorphic Computing
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Author : Su-Ting Han
language : en
Publisher: Woodhead Publishing
Release Date : 2020-05-26
Photo Electroactive Non Volatile Memories For Data Storage And Neuromorphic Computing written by Su-Ting Han and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-26 with Technology & Engineering categories.
Photo-Electroactive Non-Volatile Memories for Data Storage and Neuromorphic Computing summarizes advances in the development of photo-electroactive memories and neuromorphic computing systems, suggests possible solutions to the challenges of device design, and evaluates the prospects for commercial applications. Sections covers developments in electro-photoactive memory, and photonic neuromorphic and in-memory computing, including discussions on design concepts, operation principles and basic storage mechanism of optoelectronic memory devices, potential materials from organic molecules, semiconductor quantum dots to two-dimensional materials with desirable electrical and optical properties, device challenges, and possible strategies. This comprehensive, accessible and up-to-date book will be of particular interest to graduate students and researchers in solid-state electronics. It is an invaluable systematic introduction to the memory characteristics, operation principles and storage mechanisms of the latest reported electro-photoactive memory devices. - Reviews the most promising materials to enable emerging computing memory and data storage devices, including one- and two-dimensional materials, metal oxides, semiconductors, organic materials, and more - Discusses fundamental mechanisms and design strategies for two- and three-terminal device structures - Addresses device challenges and strategies to enable translation of optical and optoelectronic technologies
Recent Advances And Emerging Challenges In Stem
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Author : Yadir Torres
language : en
Publisher: Springer Nature
Release Date : 2024-08-11
Recent Advances And Emerging Challenges In Stem written by Yadir Torres and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-11 with Technology & Engineering categories.
This book covers the works presented at the workshop aimed at bridging academia, industry, and society, fostering collaboration among lecturers, technicians, researchers, and students. It delves into functional materials, tribo-mechanical behavior, bio-residues, and biopolymers. Some chapters explore computer architecture, artificial intelligence, smart cities, sustainable industries, and life cycle assessment. Additionally, the book addresses emerging pollutants, cellular and bacterial behavior, powder technology, applied physics, applied mathematics, industrial and environmental chemistry, product design, industrial robotic systems, and deep learning systems.
Understanding And Bridging The Gap Between Neuromorphic Computing And Machine Learning Volume Ii
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Author : Huajin Tang
language : en
Publisher: Frontiers Media SA
Release Date : 2024-08-26
Understanding And Bridging The Gap Between Neuromorphic Computing And Machine Learning Volume Ii written by Huajin Tang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-26 with Science categories.
Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.