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Author: Toshiyuki Yamane
Publisher: Frontiers Media SA
ISBN: 2832531288
Category : Science
Languages : en
Pages : 163
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Book Description
Author: Toshiyuki Yamane
Publisher: Frontiers Media SA
ISBN: 2832531288
Category : Science
Languages : en
Pages : 163
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Book Description
Author: Robert Kozma
Publisher: Springer Science & Business Media
ISBN: 9400744919
Category : Medical
Languages : en
Pages : 318
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Book Description
Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. The possibility of gathering prominent scientists in the heart of the Silicon Valley given by the 2011 International Joint Conference on Neural Networks held in San Jose, CA, has offered us the unique opportunity of organizing a series of special events on the present status and future perspectives in neuromorphic memristor science. This book presents a selection of the remarkable contributions given by the leaders of the field and it may serve as inspiration and future reference to all researchers that want to explore the extraordinary possibilities given by this revolutionary concept.
Author: National Academies of Sciences Engineering and Medicine
Publisher:
ISBN: 9780309683197
Category :
Languages : en
Pages :
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Book Description
Current von Neumann style computing is energy inefficient and bandwidth limited as information is physically shuttled via electrons between processor, short term non-volatile memory, and long-term storage. Biologically inspired neuromorphic computing, with its inherent autonomous learning capabilities and much lower power requirements based on analog processing, is seen as an avenue for overcoming these limitations. The development of nanoelectronic memory resistors, or memristors, is essential to neuromorphic architectures as they allow logic-based elements for information processing to be combined directly with nonvolatile memory for efficient emulation of neurons and synapses found in the brain. Memristors are typically composed of a switchable material with nonlinear hysteretic behavior sandwiched between two conducting encoding elements. The design, dynamic control, scaling and fundamental understanding of these materials is essential for establishing memristive devices. To explore the state-of-the-art in the materials fundamentally underlying memristor technologies: their science, their mechanisms and their functional imperatives to realize neuromorphic computing machines, the National Academies of Sciences, Engineering, and Medicine's Board on Physics and Astronomy convened a workshop on February 28, 2020. This publication summarizes the presentation and discussion of the workshop.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 471
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Book Description
What Is Neuromorphic Engineering Neuromorphic computing and neuromorphic engineering are both terms that refer to the same thing: the use of very-large-scale integration (VLSI) systems that incorporate electrical analog circuits to simulate neuro-biological structures that are found in the nervous system. Any electronic device that does calculations with the help of artificial neurons that are implemented as physical structures is referred to as a neuromorphic computer or chip. Recently, the word "neuromorphic" has been used to refer to analog, digital, mixed-mode analog/digital VLSI, and software systems that embody models of brain systems. This use of the term has become more common. To actualize the implementation of neuromorphic computing on the hardware level, oxide-based memristors, spintronic memory, threshold switches, and transistors are some of the components that may be used. Training software-based neuromorphic systems of spiking neural networks can be accomplished through the use of error backpropagation, for instance through the utilization of Python-based frameworks like snnTorch, or through the utilization of canonical learning rules from the biological learning literature, for instance through the utilization of BindsNet. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuromorphic engineering Chapter 2: Artificial neuron Chapter 3: Bio-inspired computing Chapter 4: Steve Furber Chapter 5: Carver Mead Chapter 6: Recurrent neural network Chapter 7: Neural network Chapter 8: Wetware computer Chapter 9: Computational neurogenetic modeling Chapter 10: Spiking neural network Chapter 11: Neurorobotics Chapter 12: Misha Mahowald Chapter 13: Memristor Chapter 14: Physical neural network Chapter 15: NOMFET Chapter 16: Massimiliano Versace Chapter 17: Kwabena Boahen Chapter 18: SpiNNaker Chapter 19: Cognitive computer Chapter 20: Glossary of artificial intelligence Chapter 21: Hai Li (II) Answering the public top questions about neuromorphic engineering. (III) Real world examples for the usage of neuromorphic engineering in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neuromorphic engineering' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuromorphic engineering.
Author: Dhanasekar, S.
Publisher: IGI Global
ISBN: 1668465981
Category : Computers
Languages : en
Pages : 400
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Book Description
As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery-powered and deeply embedded devices are challenged to perform AI functions such as computer vision and voice recognition. Microchip Technology Inc., via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain Memory Solution. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory computer solution is ideal for an AI application. Neuromorphic Computing Systems for Industry 4.0 covers the available literature in the field of neural computing-based microchip technology. It provides further research opportunities in this dynamic field. Covering topics such as emotion recognition, biometric authentication, and neural network protection, this premier reference source is an essential resource for technology developers, computer scientists, engineers, students and educators of higher education, librarians, researchers, and academicians.
Author: Jordi Suñé
Publisher: MDPI
ISBN: 3039285769
Category : Technology & Engineering
Languages : en
Pages : 244
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Book Description
Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
Author: Haijun Zhang
Publisher: Springer Nature
ISBN: 9819770041
Category :
Languages : en
Pages : 367
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Book Description
Author: Haijun Zhang
Publisher: Springer Nature
ISBN: 9819958474
Category : Computers
Languages : en
Pages : 627
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Book Description
The two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023. The 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. The papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Machine learning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, Machine Learning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and Their Engineering Applications; and Other Neural computing-related topics.
Author: S. Dhanasekar
Publisher: Engineering Science Reference
ISBN: 9781668465967
Category :
Languages : en
Pages : 0
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Book Description
"The comprehensive and timely publication aims to be an essential reference source, on the available literature in the field of Neural computing-based Microchip Technology. This will provide further research opportunities in these dynamic fields. It is hoped that this handbook will provide the resources necessary for researchers, advanced level students, technology developers to adopt and implement the advances in technology and applications"--
Author: Ovidiu Vermesan
Publisher: CRC Press
ISBN: 1000881911
Category : Computers
Languages : en
Pages : 143
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Book Description
Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.
