This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book 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 book 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.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book 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 book 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.

The book presents the proceedings of the 2nd International Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took place on December 4-5, 2018 in Nanjing, People’s Republic of China. The aim of this conference is to bring together researchers and developers as well as regulators and policy makers to present their latest views on 5G, including new networking, new wireless communications, resource control & management, future access techniques, new emerging applications, and latest findings in key research activities on 5G. The book is applicable to researchers, academics, students, and professionals.

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2013, held in Tokyo, Japan, in December 2013. The 67 revised full papers presented were carefully reviewed and selected from 141 submissions. The papers and 2 invited talks cover a wide range of topics such as mobile applications, social networks, networking, data management and services.

This book introduces two basic big data processing paradigms for batch data and streaming data.  Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies.

The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing.

Cloud Networking for Big Data

Thisbook covers three major parts of Big Data: concepts, theories andapplications. Written by world-renowned leaders in Big Data, this book exploresthe problems, possible solutions and directions for Big Data in research andpractice.  It also focuses on high level concepts such as definitionsof Big Data from different angles; surveys in research and applications; andexisting tools, mechanisms, and systems in practice. Each chapter is independentfrom the other chapters, allowing users to read any chapter directly.

After examining the practical sideof Big Data, this book presents theoretical perspectives. The theoreticalresearch ranges from Big Data representation, modeling and topology todistribution and dimension reducing.  Chapters also investigate the manydisciplines that involve Big Data, such as statistics, data mining, machinelearning, networking, algorithms, security and differential geometry. The lastsection of this book introduces Big Data applications from differentcommunities, such as business, engineering and science.

Thisbook constitutes the thoroughly refereed proceedings of the 11th InternationalConference on Collaborative Computing: Networking, Applications, andWorksharing, CollaborateCom 2015, held in Wuhan, China, in November  2015.

The 24 full papers and 8 short papers presented were carefullyreviewed and selected from numerous submissions. They address topics aroundnetworking, technology and systems, including but not limited to collaborativecloud computing, architecture and evaluation, collaborative applications,sensors and Internet of Things (IoT), security.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book 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 book 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.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book 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 book 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.

The book presents the proceedings of the 2nd International Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took place on December 4-5, 2018 in Nanjing, People’s Republic of China. The aim of this conference is to bring together researchers and developers as well as regulators and policy makers to present their latest views on 5G, including new networking, new wireless communications, resource control & management, future access techniques, new emerging applications, and latest findings in key research activities on 5G. The book is applicable to researchers, academics, students, and professionals.

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2013, held in Tokyo, Japan, in December 2013. The 67 revised full papers presented were carefully reviewed and selected from 141 submissions. The papers and 2 invited talks cover a wide range of topics such as mobile applications, social networks, networking, data management and services.

This book introduces two basic big data processing paradigms for batch data and streaming data.  Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies.

The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing.

Cloud Networking for Big Data

Thisbook covers three major parts of Big Data: concepts, theories andapplications. Written by world-renowned leaders in Big Data, this book exploresthe problems, possible solutions and directions for Big Data in research andpractice.  It also focuses on high level concepts such as definitionsof Big Data from different angles; surveys in research and applications; andexisting tools, mechanisms, and systems in practice. Each chapter is independentfrom the other chapters, allowing users to read any chapter directly.

After examining the practical sideof Big Data, this book presents theoretical perspectives. The theoreticalresearch ranges from Big Data representation, modeling and topology todistribution and dimension reducing.  Chapters also investigate the manydisciplines that involve Big Data, such as statistics, data mining, machinelearning, networking, algorithms, security and differential geometry. The lastsection of this book introduces Big Data applications from differentcommunities, such as business, engineering and science.

Thisbook constitutes the thoroughly refereed proceedings of the 11th InternationalConference on Collaborative Computing: Networking, Applications, andWorksharing, CollaborateCom 2015, held in Wuhan, China, in November  2015.

The 24 full papers and 8 short papers presented were carefullyreviewed and selected from numerous submissions. They address topics aroundnetworking, technology and systems, including but not limited to collaborativecloud computing, architecture and evaluation, collaborative applications,sensors and Internet of Things (IoT), security.