Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing PDF Download
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Author: Sudeep Pasricha
Publisher: Springer
ISBN: 9783031195679
Category : Technology & Engineering
Languages : en
Pages : 0
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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.
Author: Sudeep Pasricha
Publisher: Springer
ISBN: 9783031195679
Category : Technology & Engineering
Languages : en
Pages : 0
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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.
Author: Sudeep Pasricha
Publisher: Springer Nature
ISBN: 3031399323
Category : Technology & Engineering
Languages : en
Pages : 481
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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. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Author: Sudeep Pasricha
Publisher: Springer Nature
ISBN: 303140677X
Category : Technology & Engineering
Languages : en
Pages : 571
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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. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Author: Dylan D. Schmorrow
Publisher: Springer Nature
ISBN: 3031615727
Category :
Languages : en
Pages : 282
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Book Description
Author: Goel, Neha
Publisher: IGI Global
ISBN: 166848787X
Category : Computers
Languages : en
Pages : 570
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Book Description
Machine learning (ML) and the internet of things (IoT) are the top technologies used by businesses to increase efficiency, productivity, and competitiveness in this fast-paced digital era transformation. ML is the key tool for fast processing and decision making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. IoT technology has proven efficient in solving many real-world problems, and ML algorithms combined with IoT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. The Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries highlights the importance of ML for IoT’s success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Covering topics such as agriculture, pattern recognition, and smart applications, this premier reference source is an essential resource for engineers, scientists, educators, students, researchers, and academicians.
Author: Mahdi Nikdast
Publisher: CRC Press
ISBN: 1000480143
Category : Technology & Engineering
Languages : en
Pages : 391
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Book Description
Silicon photonics is beginning to play an important role in driving innovations in communication and computation for an increasing number of applications, from health care and biomedical sensors to autonomous driving, datacenter networking, and security. In recent years, there has been a significant amount of effort in industry and academia to innovate, design, develop, analyze, optimize, and fabricate systems employing silicon photonics, shaping the future of not only Datacom and telecom technology but also high-performance computing and emerging computing paradigms, such as optical computing and artificial intelligence. Different from existing books in this area, Silicon Photonics for High-Performance Computing and Beyond presents a comprehensive overview of the current state-of-the-art technology and research achievements in applying silicon photonics for communication and computation. It focuses on various design, development, and integration challenges, reviews the latest advances spanning materials, devices, circuits, systems, and applications. Technical topics discussed in the book include: • Requirements and the latest advances in high-performance computing systems • Device- and system-level challenges and latest improvements to deploy silicon photonics in computing systems • Novel design solutions and design automation techniques for silicon photonic integrated circuits • Novel materials, devices, and photonic integrated circuits on silicon • Emerging computing technologies and applications based on silicon photonics Silicon Photonics for High-Performance Computing and Beyond presents a compilation of 19 outstanding contributions from academic and industry pioneers in the field. The selected contributions present insightful discussions and innovative approaches to understand current and future bottlenecks in high-performance computing systems and traditional computing platforms, and the promise of silicon photonics to address those challenges. It is ideal for researchers and engineers working in the photonics, electrical, and computer engineering industries as well as academic researchers and graduate students (M.S. and Ph.D.) in computer science and engineering, electronic and electrical engineering, applied physics, photonics, and optics.
Author: Rajiv Pandey
Publisher: Academic Press
ISBN: 0128240555
Category : Science
Languages : en
Pages : 516
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Book Description
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
Author: Nitin Liladhar Rane
Publisher: Deep Science Publishing
ISBN: 8198127186
Category : Computers
Languages : en
Pages : 185
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Book Description
This book offers an insight into the applications of Artificial Intelligence (AI)- Machine Learning Algorithms and Deep Learning (DL) in Bigdata Analytics to Industry 4.0/5.0 and Society 5.0 with transformative power responsibly. It has delved into how these technologies are disrupting industries, fostering innovation, and solving age-old social problems-so that readers have an understanding of where the digital world is headed. These chapters cover the big picture subjects of using AI with Big data analytics aimed mostly at increasing industrial efficiency, healthcare optimization, retail transformation, construction industry transformation, autonomous vehicles development and environmental sustainability improvement. The book covers each of these technologies extensively applied to full chapters devoted to detail studies, methodologies and practical usages. One of the central concepts in the book is how we evolve from industry 4.0 to industry 5.0. Therefore, Industry 4.0 relies on the automation and data exchange in manufacturing technologies using cyber-physical systems, the Internet of Things and cloud computing route to intelligent factories. During this phase, it improves operational efficiency, predictive maintenance and real-time monitoring which lowers down time and other operating costs by considerable amount. As industries move towards Industry 5.0, a lot has been noted-human-oriented solutions that combine human creativity and intelligence with highly automated and distributed technological tools. More cooperation between humans and machines during such times will, therefore, result in more customized production aimed at sustainable processes. The book details how, thanks to digital twins-that is, innumerable virtual replicas of physical systems-the further step is taken, allowing for real-time data analysis and, consequently innovative ways of manufacturing where the interests of the workers and customers come first. The present book discusses how AI and big data analytics transcend industrial applications to meet more societal ends as society ushers in its fifth revolution. Society 5.0 postulates that a super-smart digital society will drive transformation in all aspects of life, ranging from health and education to planning urban resources and infrastructure and ensuring public safety. The combination of AI with Big Data makes personalized healthcare services possible, competent resource planning in cities, and environmental sustainability in place via predictive analytics or simulation models. One such industry in which significant changes are coming, according to AI and Big Data analytics, is healthcare. This book shows how these technologies improve diagnostic accuracy, enable personalized treatment plans, and optimize resource allocations. Predictive insights can predict outbreaks and admissions, which helps better preparedness against diseases and also optimizes health resource utilization. AI in medical imaging and anomaly detection strengthens the efficiency of professional health experts, thus delivering better patient outcomes. AI and big data analytics have further remodelled the retail industry by providing retailers profound insights into consumer behaviour and preferences. With this information, retailers can adopt person-segmented marketing techniques and optimize inventory levels while enabling high levels of customer service using AI-fuelled chatbots and virtual assistants. These technologies help retailers stay competitive in an ever-developing market environment by offering solutions structured based on individual needs expressed by customers. AI and big data analytics combine to form one synergy connected with autonomous vehicles. It further goes on to discuss the huge amount of data needed for training these AI models and big data analytics in refining the accuracy and safety of autonomous driving systems. Another critical area in which AI and Big Data Analytics make a considerable impact is environmental sustainability. By applying these analyses to large data sets relating to climatic changes, energy consumption, and natural resources, AI models can establish trends and recognize patterns indicating future changes. This predictive ability equips organizations and governments with tools to develop lower environmental footprints and promote sustainable practices proactively. It further explains AI-enabled energy management systems that drive optimized energy use in buildings to reduce carbon emissions and save on associated costs. This certainly looks like something for a vast readership: it speaks more to academics, professionals working in the industry, and decision-makers-but, really, to anybody who seeks to grasp the transformative powerfulness of AI and big data analytics. This source will provide information on overall guidance and a rich source of inspiration in using these technologies to enable innovation and sustainable development across different sectors. Actual case examples and practical applications are given to convey the knowledge and elements that readers need to know as they go about using AI and big data analytics. This book also includes discussions concerning the dynamic policy and regulatory scenes of AI, pointing out that it is necessary to have standard policies that should be implemented to have ethical deployment of AI that reduces risks. This book also focuses on challenges in implementing AI for intelligent and sustainable industries, meaning technical, ethical, and operational barriers. It outlines high costs, low-quality data, and the need for skilled professionals; ethical concerns and robust cybersecurity measures become necessary. As such, this book will engross an audience ranging from academics to industry professionals and policymakers working toward understanding and using AI and big data for sustainable development and technological advancement.
Author: Pete Warden
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
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Book Description
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
Author: D. Jude Hemanth
Publisher: Elsevier
ISBN: 0323994334
Category : Technology & Engineering
Languages : en
Pages : 254
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Book Description
Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students. - Introduces and provides reviews on cyber physical and edge computing systems, with different architectures and models needed to address sustainable solutions to social, environmental and economic applications - Presents the different architectures of edge computing for building cyber physical systems with dynamic resource provisioning and security solutions - Provides AI based perspectives to edge-based cyber physical systems with different algorithms and AI based security solutions - Covers different case studies and applications in detail, with real-life examples and possible challenges that can be encountered - Offers perspectives for the design, development and commissioning of intelligent edge-based cyber physical systems
