Compact and Fast Machine Learning Accelerator for IoT Devices - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Compact and Fast Machine Learning Accelerator for IoT Devices write by Hantao Huang. This book was released on 2018-12-07. Compact and Fast Machine Learning Accelerator for IoT Devices available in PDF, EPUB and Kindle. This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
Machine Learning in IoT Systems
Machine Learning in IoT Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in IoT Systems write by Mohsen Imani. This book was released on 2020. Machine Learning in IoT Systems available in PDF, EPUB and Kindle. With the emergence of the Internet of Things (IoT), devices are generating massive amounts of data. Running machine learning algorithms on IoT devices poses substantial technical challenges due to their limited resources. The focus of this dissertation is to dramatically increase computing efficiency as well as the learning capability of today's IoT systems by accelerating existing algorithms in hardware and designing new classes of light-weight machine learning algorithms. Our design makes a modification to storage-class memory to support search-based and vector-based computation in memory. We show how this architecture can be used to accelerate deep neural networks in both training and inference phases, resulting in 303x faster and 48x more energy efficient training as compared to the state-of-the-art GPU. Hardware acceleration alone does not provide all the efficiency and robustness that we need. Therefore, we present Hyperdimensional (HD) computing, an alternative method of learning that implements principles of the functionality in the brain: (i) fast learning, (ii) robustness to noise/error, and (iii) intertwined memory and logic. These features make HD computing a promising solution for today's embedded devices with limited resources as well as future computing systems in deep nanoscaled technology that have issues of high noise and variability. We exploit emerging technologies to enable processing in-memory which is capable of highly-parallel computation and data movement reduction. Our evaluations show that HD computing provides 39X faster and 56X more energy efficiency as compared to state-of-the-art deep learning accelerator.
Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries
Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries write by Goel, Neha. This book was released on 2023-07-03. Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries available in PDF, EPUB and Kindle. 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.
Hands-On Deep Learning for IoT
Hands-On Deep Learning for IoT - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hands-On Deep Learning for IoT write by Md. Rezaul Karim. This book was released on 2019-06-27. Hands-On Deep Learning for IoT available in PDF, EPUB and Kindle. Implement popular deep learning techniques to make your IoT applications smarter Key FeaturesUnderstand how deep learning facilitates fast and accurate analytics in IoTBuild intelligent voice and speech recognition apps in TensorFlow and ChainerAnalyze IoT data for making automated decisions and efficient predictionsBook Description Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making. What you will learnGet acquainted with different neural network architectures and their suitability in IoTUnderstand how deep learning can improve the predictive power in your IoT solutionsCapture and process streaming data for predictive maintenanceSelect optimal frameworks for image recognition and indoor localizationAnalyze voice data for speech recognition in IoT applicationsDevelop deep learning-based IoT solutions for healthcareEnhance security in your IoT solutionsVisualize analyzed data to uncover insights and perform accurate predictionsWho this book is for If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.
Federated Learning for IoT Applications
Federated Learning for IoT Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Federated Learning for IoT Applications write by Satya Prakash Yadav. This book was released on 2022-02-02. Federated Learning for IoT Applications available in PDF, EPUB and Kindle. This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
