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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.
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.
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.
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
Author: Ji Lin (Researcher in computer science)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables various real-world applications (e.g., keyword spotting, anomaly detection). However, deploying deep learning models to MCUs is challenging due to the limited memory size: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. In this thesis, we study efficient algorithms and systems for tiny-scale deep learning. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer-wise optimization, reducing the memory usage by 3.4x, and accelerating the inference by 1.7-3.3x compared to TF-Lite Micro and CMSIS-NN. For vision applications on MCUs, we diagnosed and found that existing convolutional neural network (CNN) designs have an imbalanced peak memory distribution: the first several layers have much higher peak memory usage than the rest of the network. Based on the observation, we further extend the framework to support patch-based inference to break the memory bottleneck of the initial stage. MCUNet is the first to achieves>70% ImageNet top1 accuracy on an off-the-shelf commercial microcontroller, using 3.5x less SRAM and 5.7x less Flash compared to quantized MobileNetV2 and ResNet-18. On visual & audio wake words tasks, MCUNet achieves state-of-the-art accuracy and runs 2.4- 3.4x faster than MobileNetV2 and ProxylessNAS-based solutions with 3.7-4.1x smaller peak SRAM. Our study suggests that the era of always-on tiny machine learning on IoT devices has arrived.
Author: Hantao Huang
Publisher: Springer
ISBN: 9811333238
Category : Technology & Engineering
Languages : en
Pages : 149
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Book Description
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: 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.
Author: Rohit Sharma
Publisher: AITS Inc
ISBN:
Category : Business & Economics
Languages : en
Pages : 182
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Book Description
This book is an effort by AI Technology & Systems to demystify the TinyML technology including market, applications, algorithms, tools and technology. the book dive deeper into the technology beyond common application and keep it light for the readers with varying background including students, hobbyists, managers, market researchers and developers. It starts with introduction to TinyML with benefits and scalability. It introduces no-code and low-code tinyML platform to develop production worthy solutions including audio wake word, visual wake word, American sign language and predictive maintenance. Last two chapters are devoted to sensor and hardware agnostic autoML and tinyML compiler technologies. More information at http://thetinymlbook.com/
Author: Krishna Kant Singh
Publisher: John Wiley & Sons
ISBN: 1119761875
Category : Computers
Languages : en
Pages : 258
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Book Description
MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAIN The unique aspect of this book is that its focus is the convergence of machine learning, IoT, and blockchain in a single publication. Blockchain technology and the Internet of Things (IoT) are two of the most impactful trends to have emerged in the field of machine learning. Although there are a number of books available solely on the subjects of machine learning, IoT and blockchain technology, no such book has been available which focuses on machine learning techniques for IoT and blockchain convergence until now. Thus, this book is unique in terms of the topics it covers. Designed as an essential guide for all academicians, researchers, and those in industry who are working in related fields, this book will provide insights into the convergence of blockchain technology and the IoT with machine learning. Highlights of the book include: Examines many industries such as agriculture, manufacturing, food production, healthcare, the military, and IT Security of the Internet of Things using blockchain and AI Developing smart cities and transportation systems using machine learning and IoT Audience The target audience of this book is professionals and researchers (artificial intelligence specialists, systems engineers, information technologists) in the fields of machine learning, IoT, and blockchain technology.
Author: Valentina E. Balas
Publisher: Springer Nature
ISBN: 303090119X
Category : Technology & Engineering
Languages : en
Pages : 340
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Book Description
This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.
Author: Neeraj Kumar
Publisher: CRC Press
ISBN: 1000767590
Category : Computers
Languages : en
Pages : 319
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Book Description
This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions
