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.

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

This holistic book is an invaluable reference for addressing various practical challenges in architecting and engineering Intelligent IoT and eHealth solutions for industry practitioners, academic and researchers, as well as for engineers involved in product development. The first part provides a comprehensive guide to fundamentals, applications, challenges, technical and economic benefits, and promises of the Internet of Things using examples of real-world applications. It also addresses all important aspects of designing and engineering cutting-edge IoT solutions using a cross-layer approach from device to fog, and cloud covering standards, protocols, design principles, reference architectures, as well as all the underlying technologies, pillars, and components such as embedded systems, network, cloud computing, data storage, data processing, big data analytics, machine learning, distributed ledger technologies, and security. In addition, it discusses the effects of Intelligent IoT, which are reflected in new business models and digital transformation. The second part provides an insightful guide to the design and deployment of IoT solutions for smart healthcare as one of the most important applications of IoT. Therefore, the second part targets smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics that are aimed at providing connected health interventions to individuals for healthier lifestyles.

The book deals with the conceptual and practical knowledge of the latest tools and methodologies of hardware development for Internet of Things (IoT) and variety of real-world challenges. The topics cover the state-of-the-art and future perspectives of IoT technologies, where industry experts, researchers, and academics had shared ideas and experiences surrounding frontier technologies, breakthrough, and innovative solutions and applications. Several aspects of various hardware technologies, methodologies, and communication protocol such as formal design flow for IoT hardware, design approaches for IoT hardware, IoT solution reference architectures and Instances, simulation, modelling and programming framework, hardware basics of sensors for IoT, configurable processor and technology for IoT and real-life examples and studies are critically examined in this book. It also identifies key technological facet that supports the relevance of hardware perspective of IoT and discusses the benefits and challenges to dominate the next decades. The book serves as an excellent reference for senior undergraduates and graduates in electrical and computer engineering, research scholars, mobile and wireless communications engineers, IT engineers, and electronics engineers who need to understand IoT at an in-depth level to build and manage IoT solutions.

This book sets the innovative research contributions, works, and solutions for almost all the intelligent and smart applications in the smart cities. The smart city concept is a relevant topic for industrials, governments, and citizens. Due to this, the smart city, considered as a multi-domain context, attracts tremendously academics researchers and practitioners who provide efforts in theoretical proofs, approaches, architectures, and in applied researches. The importance of smart cities comes essentially from the significant growth of populations in the near future which conducts to a real need of smart applications that can support this evolution in the future cities. The main scope of this book covers new and original ideas for the next generations of cities using the new technologies. The book involves the application of the data science and AI, IoT technologies and architectures, smart earth and water management, smart education and E-learning systems, smart modeling systems, smart mobility, and renewable energy. It also reports recent research works on big data technologies, image processing and recognition systems, and smart security and privacy.

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.