Machine Learning on Commodity Tiny Devices

Machine Learning on Commodity Tiny Devices PDF Author: Song Guo
Publisher: CRC Press
ISBN: 100078035X
Category : Computers
Languages : en
Pages : 268

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Book Description
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.

Machine Learning on Commodity Tiny Devices

Machine Learning on Commodity Tiny Devices PDF Author: Song Guo
Publisher: CRC Press
ISBN: 100078035X
Category : Computers
Languages : en
Pages : 268

Get Book Here

Book Description
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.

18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023)

18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) PDF Author: Pablo García Bringas
Publisher: Springer Nature
ISBN: 3031425294
Category : Technology & Engineering
Languages : en
Pages : 305

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Book Description
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.

TinyML

TinyML PDF 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

TinyML for Edge Intelligence in IoT and LPWAN Networks

TinyML for Edge Intelligence in IoT and LPWAN Networks PDF Author: Bharat S Chaudhari
Publisher: Elsevier
ISBN: 0443222037
Category : Computers
Languages : en
Pages : 520

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Book Description
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.

Machine Learning and Artificial Intelligence for Agricultural Economics

Machine Learning and Artificial Intelligence for Agricultural Economics PDF Author: Chandrasekar Vuppalapati
Publisher: Springer Nature
ISBN: 3030774856
Category : Business & Economics
Languages : en
Pages : 611

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Book Description
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.

Machine Learning for Cyber Security

Machine Learning for Cyber Security PDF Author: Preeti Malik
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110766760
Category : Business & Economics
Languages : en
Pages : 170

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Book Description
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.

Low-Power Computer Vision

Low-Power Computer Vision PDF Author: George K. Thiruvathukal
Publisher: CRC Press
ISBN: 1000540960
Category : Computers
Languages : en
Pages : 395

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Book Description
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.

Intelligent Control for Modern Transportation Systems

Intelligent Control for Modern Transportation Systems PDF Author: Arunesh Kumar Singh
Publisher: CRC Press
ISBN: 1000963527
Category : Technology & Engineering
Languages : en
Pages : 194

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Book Description
The book comprehensively discusses concepts of artificial intelligence in green transportation systems. It further covers intelligent techniques for precise modeling of complex transportation infrastructure, forecasting and predicting traffic congestion, and intelligent control techniques for maximizing performance and safety. It further provides MATLAB® programs for artificial intelligence techniques. It discusses artificial intelligence-based approaches and technologies in controlling and operating solar photovoltaic systems to generate power for electric vehicles. Highlights how different technological advancements have revolutionized the transportation system. Presents core concepts and principles of soft computing techniques in the control and management of modern transportation systems. Discusses important topics such as speed control, fuel control challenges, transport infrastructure modeling, and safety analysis. Showcases MATLAB® programs for artificial intelligence techniques. Discusses roles, implementation, and approaches of different intelligent techniques in the field of transportation systems. It will serve as an ideal text for professionals, graduate students, and academicians in the fields of electrical engineering, electronics and communication engineering, civil engineering, and computer engineering.

A.I. in 2020

A.I. in 2020 PDF Author: Jair Ribeiro
Publisher: Jair Ribeiro
ISBN:
Category : Computers
Languages : en
Pages : 273

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Book Description
This book collects the best articles about several artificial intelligence concepts that I have published online during 2020. It is dedicated to anyone interested in Artificial Intelligence and anyone who wants to understand some of the building blocks that form this fascinating technology. Here, you will find my best articles, updated and revisited, with some more insights, with a suitable format for book readers. The content of this book results from extensive research, long nights of studies, and some of my best years of work in the field in some prestigious enterprise companies in Europe. My goal is to share as much as possible through an affordable, simple, and straightforward language, valuable knowledge that helps you understanding complex topics related to technologies such as Machine Learning, Deep Learning, Analytics, and Autonomous Vehicles, among others. It is a satisfying adventure, I must say. Every day I receive considerably positive feedback, lots of article views, lots of likes, retweets, and more on my social networks and not less, some indications as a top writer, invitations to collaborate in some prestigious online publications. All this is truly motivating. I believe that life is complicated enough, so I consider that every time someone tries to simplify concepts and knowledge useful to humanity, this can be regarded as an essential contribution to inclusiveness and equity in the world. So, this is my mission. This book is not intended to exhaust all the learning needs of those wishing to enter the AI world. It is a starting point composed of some “scattered notes” that will help you put together some valuable pieces of technology's great mosaic. The articles presented here are very beneficial to provide you a practical introduction to some of the most important concepts that many of us face daily. They also will give you some pointers on how to go beyond the first step in search of much more. Just as Dante suggested: “You were not meant to live as ugly, but to seek virtue and knowledge.”

Deep Learning for Computer Architects

Deep Learning for Computer Architects PDF Author: Brandon Reagen
Publisher: Springer Nature
ISBN: 3031017560
Category : Technology & Engineering
Languages : en
Pages : 109

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Book Description
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.