Machine Learning On Commodity Tiny Devices
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| Author | : Song Guo |
| Publisher | : CRC Press |
| Total Pages | : 268 |
| Release | : 2022-11-24 |
| Genre | : Computers |
| ISBN | : 100078035X |
This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This volume will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.
| Author | : Pete Warden |
| Publisher | : O'Reilly Media |
| Total Pages | : 504 |
| Release | : 2019-12-16 |
| Genre | : Computers |
| ISBN | : 1492052019 |
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 | : Pablo García Bringas |
| Publisher | : Springer Nature |
| Total Pages | : 305 |
| Release | : 2023-08-30 |
| Genre | : Technology & Engineering |
| ISBN | : 3031425294 |
This book of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2023 conference held in the beautiful and historic city of Salamanca (Spain) in September 2023. Soft computing represents a collection or set of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the 18th SOCO 2023 International Program Committee selected 61 papers which are published in these conference proceedings and represents an acceptance rate of 60%. In this relevant edition, a particular emphasis was put on the organization of special sessions. Seven special sessions were organized related to relevant topics such as: Time Series Forecasting in Industrial and Environmental Applications, Technological Foundations and Advanced Applications of Drone Systems, Soft Computing Methods in Manufacturing and Management Systems, Efficiency and Explainability in Machine Learning and Soft Computing, Machine Learning and Computer Vision in Industry 4.0, Genetic and Evolutionary Computation in Real World and Industry, and Soft Computing and Hard Computing for a Data Science Process Model. The selection of papers was extremely rigorous to maintain the high quality of the conference. We want to thank the members of the Program Committees for their hard work during the reviewing process. This is a crucial process for creating a high-standard conference; the SOCO conference would not exist without their help.
| Author | : Chandrasekar Vuppalapati |
| Publisher | : Springer Nature |
| Total Pages | : 611 |
| Release | : 2021-10-04 |
| Genre | : Business & Economics |
| ISBN | : 3030774856 |
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
| Author | : Bharat S Chaudhari |
| Publisher | : Elsevier |
| Total Pages | : 520 |
| Release | : 2024-05-29 |
| Genre | : Computers |
| ISBN | : 0443222037 |
Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded IoT and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like TinyML. TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of TinyML. Practical applications of the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies. TinyML for Edge Intelligence in IoT and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes. - This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications. - The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications. - Applications from the healthcare and industrial sectors are presented. - Guidance on the design of applications and the selection of appropriate technologies is provided.
| Author | : R. Anandan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 498 |
| Release | : 2021-11-16 |
| Genre | : Computers |
| ISBN | : 1119750598 |
HUMAN COMMUNICATION TECHNOLOGY A unique book explaining how perception, location, communication, cognition, computation, networking, propulsion, integration of federated Internet of Robotic Things (IoRT) and digital platforms are important components of new-generation IoRT applications through continuous, real-time interaction with the world. The 16 chapters in this book discuss new architectures, networking paradigms, trustworthy structures, and platforms for the integration of applications across various business and industrial domains that are needed for the emergence of intelligent things (static or mobile) in collaborative autonomous fleets. These new apps speed up the progress of paradigms of autonomous system design and the proliferation of the Internet of Robotic Things (IoRT). Collaborative robotic things can communicate with other things in the IoRT, learn independently, interact securely with the world, people, and other things, and acquire characteristics that make them self-maintaining, self-aware, self-healing, and fail-safe operational. Due to the ubiquitous nature of collaborative robotic things, the IoRT, which binds together the sensors and the objects of robotic things, is gaining popularity. Therefore, the information contained in this book will provide readers with a better understanding of this interdisciplinary field. Audience Researchers in various fields including computer science, IoT, artificial intelligence, machine learning, and big data analytics.
| Author | : Hantao Huang |
| Publisher | : Springer |
| Total Pages | : 157 |
| Release | : 2018-12-07 |
| Genre | : Technology & Engineering |
| ISBN | : 9811333238 |
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.
| Author | : Preeti Malik |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 170 |
| Release | : 2022-12-05 |
| Genre | : Business & Economics |
| ISBN | : 3110766760 |
This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.
| Author | : Tamás Bányai |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 227 |
| Release | : 2022-07-27 |
| Genre | : Business & Economics |
| ISBN | : 1803553723 |
The Fourth Industrial Revolution has led to revolutionary changes in production and service processes. This book explains and examines the impact of Industry 4.0 technologies on supply chain solutions. It discusses the concept, design, and implementation of supply chain solutions using Industry 4.0 technologies. Chapters address such topics as supply webs, open innovation practices, lean manufacturing, the Internet of Things, green supply chain solutions, and much more.
| Author | : George K. Thiruvathukal |
| Publisher | : CRC Press |
| Total Pages | : 395 |
| Release | : 2022-02-22 |
| Genre | : Computers |
| ISBN | : 1000540960 |
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.
