Energy Efficient Computing & Electronics


Book Description

In our abundant computing infrastructure, performance improvements across most all application spaces are now severely limited by the energy dissipation involved in processing, storing, and moving data. The exponential increase in the volume of data to be handled by our computational infrastructure is driven in large part by unstructured data from countless sources. This book explores revolutionary device concepts, associated circuits, and architectures that will greatly extend the practical engineering limits of energy-efficient computation from device to circuit to system level. With chapters written by international experts in their corresponding field, the text investigates new approaches to lower energy requirements in computing. Features • Has a comprehensive coverage of various technologies • Written by international experts in their corresponding field • Covers revolutionary concepts at the device, circuit, and system levels




Edge Computing Resilience


Book Description




Advances on Computational Intelligence in Energy


Book Description

Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.




Energy Conservation Solutions for Fog-Edge Computing Paradigms


Book Description

This book focuses on energy efficiency concerns in fog–edge computing and the requirements related to Industry 4.0 and next-generation networks like 5G and 6G. This book guides the research community about practical approaches, methodological, and moral questions in any nations’ journey to conserve energy in fog–edge computing environments. It discusses a detailed approach required to conserve energy and comparative case studies with respect to various performance evaluation metrics, such as energy conservation, resource allocation strategies, task allocation strategies, VM migration, and load-sharing strategies with state-of-the-art approaches, with fog and edge networks.




Artificial Neural Networks and Machine Learning – ICANN 2022


Book Description

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.




Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time


Book Description

Recently, several fog computing applications have been developed like IoT-based healthcare, 5G, blockchains, autonomous driving, and mobile wireless applications. They also address challenges such as data management, scalability, regulations, interoperability, device network human interfaces, security, and privacy. Further study on these applications is required to ensure this technology is utilized appropriately. Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time focuses on fog computing problems and solutions for various applications and covers the new approaches, architecture, and theoretical foundations in the fog paradigm of storage, communication, and computing. The book explores recent trends and challenges that lead to a potential course for the ideas, practices, norms, and strategies related to fog computing. Covering key topics such as data privacy, data analytics, and the internet of things, this reference work is ideal for computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.




Algorithms and Architectures for Parallel Processing


Book Description

The three volume set LNCS 13155, 13156, and 13157 constitutes the refereed proceedings of the 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021, which was held online during December 3-5, 2021. The total of 145 full papers included in these proceedings were carefully reviewed and selected from 403 submissions. They cover the many dimensions of parallel algorithms and architectures including fundamental theoretical approaches, practical experimental projects, and commercial components and systems. The papers were organized in topical sections as follows: Part I, LNCS 13155: Deep learning models and applications; software systems and efficient algorithms; edge computing and edge intelligence; service dependability and security algorithms; data science; Part II, LNCS 13156: Software systems and efficient algorithms; parallel and distributed algorithms and applications; data science; edge computing and edge intelligence; blockchain systems; deept learning models and applications; IoT; Part III, LNCS 13157: Blockchain systems; data science; distributed and network-based computing; edge computing and edge intelligence; service dependability and security algorithms; software systems and efficient algorithms.




Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing


Book Description

Nanomagnetic and spintronic computing devices are strong contenders for future replacements of CMOS. This is an important and rapidly evolving area with the semiconductor industry investing significantly in the study of nanomagnetic phenomena and in developing strategies to pinpoint and regulate nanomagnetic reliably with a high degree of energy efficiency. This timely book explores the recent and on-going research into nanomagnetic-based technology. Key features: Detailed background material and comprehensive descriptions of the current state-of-the-art research on each topic. Focuses on direct applications to devices that have potential to replace CMOS devices for computing applications such as memory, logic and higher order information processing. Discusses spin-based devices where the spin degree of freedom of charge carriers are exploited for device operation and ultimately information processing. Describes magnet switching methodologies to minimize energy dissipation. Comprehensive bibliographies included for each chapter enabling readers to conduct further research in this field. Written by internationally recognized experts, this book provides an overview of a rapidly burgeoning field for electronic device engineers, field-based applied physicists, material scientists and nanotechnologists. Furthermore, its clear and concise form equips readers with the basic understanding required to comprehend the present stage of development and to be able to contribute to future development. Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing is also an indispensable resource for students and researchers interested in computer hardware, device physics and circuits design.




Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing


Book Description

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.