Edge Artificial Intelligence
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Edge Ai
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Author : Xiaofei Wang
language : en
Publisher: Springer Nature
Release Date : 2020-08-31
Edge Ai written by Xiaofei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-31 with Computers categories.
As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
Applied Edge Ai
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Author : Pethuru Raj
language : en
Publisher: CRC Press
Release Date : 2022-04-05
Applied Edge Ai written by Pethuru Raj and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-05 with Computers categories.
The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication
Machine Learning For Edge Computing
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Author : Amitoj Singh
language : en
Publisher: CRC Press
Release Date : 2022-07-29
Machine Learning For Edge Computing written by Amitoj Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Computers categories.
This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence. The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needs Amitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.
Mobile Edge Artificial Intelligence
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Author : Yuanming Shi
language : en
Publisher: Elsevier
Release Date : 2021-08-17
Mobile Edge Artificial Intelligence written by Yuanming Shi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Computers categories.
Front Cover -- Mobile Edge Artificial Intelligence -- Copyright -- Contents -- List of figures -- Biography -- Yuanming Shi -- Kai Yang -- Zhanpeng Yang -- Yong Zhou -- Preface -- Acknowledgments -- Part 1 Introduction and overview -- 1 Motivations and organization -- 1.1 Motivations -- 1.2 Organization -- References -- 2 Primer on artificial intelligence -- 2.1 Basics of machine learning -- 2.1.1 Supervised learning -- 2.1.1.1 Logistic regression -- 2.1.1.2 Support vector machine -- 2.1.1.3 Decision tree -- 2.1.1.4 k-Nearest neighbors method -- 2.1.1.5 Neural network -- 2.1.2 Unsupervised learning -- 2.1.2.1 k-Means algorithm -- 2.1.2.2 Principal component analysis -- 2.1.2.3 Autoencoder -- 2.1.3 Reinforcement learning -- 2.1.3.1 Q-learning -- 2.1.3.2 Policy gradient -- 2.2 Models of deep learning -- 2.2.1 Convolutional neural network -- 2.2.2 Recurrent neural network -- 2.2.3 Graph neural network -- 2.2.4 Generative adversarial network -- 2.3 Summary -- References -- 3 Convex optimization -- 3.1 First-order methods -- 3.1.1 Gradient method for unconstrained problems -- 3.1.2 Gradient method for constrained problems -- 3.1.3 Subgradient descent method -- 3.1.4 Mirror descent method -- 3.1.5 Proximal gradient method -- 3.1.6 Accelerated gradient method -- 3.1.7 Smoothing for nonsmooth optimization -- 3.1.8 Dual and primal-dual methods -- 3.1.9 Alternating direction method of multipliers -- 3.1.10 Stochastic gradient method -- 3.2 Second-order methods -- 3.2.1 Newton's method -- 3.2.2 Quasi-Newton method -- 3.2.3 Gauss-Newton method -- 3.2.4 Natural gradient method -- 3.3 Summary -- References -- 4 Mobile edge AI -- 4.1 Overview -- 4.2 Edge inference -- 4.2.1 On-device inference -- 4.2.2 Edge inference via computation offloading -- 4.2.2.1 Server-based edge inference -- 4.2.2.2 Device-edge joint inference -- 4.3 Edge training.
Artificial Intelligence And Machine Learning For Edge Computing
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Author : Rajiv Pandey
language : en
Publisher: Academic Press
Release Date : 2022-04-26
Artificial Intelligence And Machine Learning For Edge Computing written by Rajiv Pandey and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-26 with Science categories.
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
Artificial Intelligence For Cloud And Edge Computing
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Author : Sanjay Misra
language : en
Publisher: Springer
Release Date : 2023-01-15
Artificial Intelligence For Cloud And Edge Computing written by Sanjay Misra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-15 with Computers categories.
This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.
Tinyml
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Author : Pete Warden
language : en
Publisher: O'Reilly Media
Release Date : 2019-12-16
Tinyml written by Pete Warden and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Computers categories.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Ai At The Edge
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Author : Daniel Situnayake
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-10
Ai At The Edge written by Daniel Situnayake and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-10 with Computers categories.
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products
Soft Computing Techniques In Engineering Health Mathematical And Social Sciences
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Author : Pradip Debnath
language : en
Publisher: CRC Press
Release Date : 2021-07-15
Soft Computing Techniques In Engineering Health Mathematical And Social Sciences written by Pradip Debnath and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Computers categories.
Soft computing techniques are no longer limited to the arena of computer science. The discipline has an exponentially growing demand in other branches of science and engineering and even into health and social science. This book contains theory and applications of soft computing in engineering, health, and social and applied sciences. Different soft computing techniques such as artificial neural networks, fuzzy systems, evolutionary algorithms and hybrid systems are discussed. It also contains important chapters in machine learning and clustering. This book presents a survey of the existing knowledge and also the current state of art development through original new contributions from the researchers. This book may be used as a one-stop reference book for a broad range of readers worldwide interested in soft computing. In each chapter, the preliminaries have been presented first and then the advanced discussion takes place. Learners and researchers from a wide variety of backgrounds will find several useful tools and techniques to develop their soft computing skills. This book is meant for graduate students, faculty and researchers willing to expand their knowledge in any branch of soft computing. The readers of this book will require minimum prerequisites of undergraduate studies in computation and mathematics.