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Embedded Systems and Artificial Intelligence by Vikrant Bhateja,Suresh Chandra Satapathy,Hassan Satori Pdf
This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2–3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
Embedded Artificial Intelligence by Ovidiu Vermesan,Mario Diaz Nava,Björn Debaillie Pdf
Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.
Beginning Artificial Intelligence with the Raspberry Pi by Donald J. Norris Pdf
Gain a gentle introduction to the world of Artificial Intelligence (AI) using the Raspberry Pi as the computing platform. Most of the major AI topics will be explored, including expert systems, machine learning both shallow and deep, fuzzy logic control, and more! AI in action will be demonstrated using the Python language on the Raspberry Pi. The Prolog language will also be introduced and used to demonstrate fundamental AI concepts. In addition, the Wolfram language will be used as part of the deep machine learning demonstrations. A series of projects will walk you through how to implement AI concepts with the Raspberry Pi. Minimal expense is needed for the projects as only a few sensors and actuators will be required. Beginners and hobbyists can jump right in to creating AI projects with the Raspberry PI using this book. What You'll Learn What AI is and—as importantly—what it is not Inference and expert systems Machine learning both shallow and deep Fuzzy logic and how to apply to an actual control system When AI might be appropriate to include in a system Constraints and limitations of the Raspberry Pi AI implementation Who This Book Is For Hobbyists, makers, engineers involved in designing autonomous systems and wanting to gain an education in fundamental AI concepts, and non-technical readers who want to understand what AI is and how it might affect their lives.
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
Learning in Embedded Systems by Leslie Pack Kaelbling Pdf
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial and error experience with an external world. The text is a detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behaviour to a complex, changing environment. Such systems include mobile robots, factory process controllers and long-term software databases.
Deep Learning on Microcontrollers by Atul Krishna Gupta,Dr. Siva Prasad Nandyala Pdf
A step-by-step guide that will teach you how to deploy TinyML on microcontrollers KEY FEATURES ● Deploy machine learning models on edge devices with ease. ● Leverage pre-built AI models and deploy them without writing any code. ● Create smart and efficient IoT solutions with TinyML. DESCRIPTION TinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you. This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers. The book will then help you build an Air gesture digit recognition system using the Arduino Nano RP2040 board and an AI project for recognizing keywords using the Syntiant TinyML board. Lastly, the book summarizes the concepts covered and provides a brief introduction to topics such as zero-shot learning, one-shot learning, federated learning, and MLOps. By the end of the book, you will be able to develop and deploy end-to-end Tiny ML solutions with ease. WHAT YOU WILL LEARN ● Learn how to build a Keyword recognition system using the Syntiant TinyML board. ● Learn how to build an air gesture digit recognition system using the Arduino Nano RP2040. ● Learn how to test and deploy models on Edge Impulse and Arduino IDE. ● Get tips to enhance system-level performance. ● Explore different real-world use cases of TinyML across various industries. WHO THIS BOOK IS FOR The book is for IoT developers, System engineers, Software engineers, Hardware engineers, and professionals who are interested in integrating AI into their work. This book is a valuable resource for Engineering undergraduates who are interested in learning about microcontrollers and IoT devices but may not know where to begin. TABLE OF CONTENTS 1. Introduction to AI 2. Traditional ML Lifecycle 3. TinyML Hardware and Software Platforms 4. End-to-End TinyML Deployment Phases 5. Real World Use Cases 6. Practical Experiments with TinyML 7. Advance Implementation with TinyML Board 8. Continuous Improvement 9. Conclusion
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by Shiho Kim,Ganesh Chandra Deka Pdf
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
AI at the Edge by Daniel Situnayake,Jenny Plunkett Pdf
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products
Applied Soft Computing and Embedded System Applications in Solar Energy by Rupendra Kumar Pachauri,Jitendra Kumar Pandey,Abhishek Sharmu,Om Nautiyal,Mangey Ram Pdf
Examines the integration of hardware with stand-alone PV panels and real time monitoring of factors affecting the efficiency of the photovoltaic panels Offers the real time implementation of soft computing and embedded system in the area of solar energy Discusses how soft computing plays a huge role in the prediction of efficiency of stand-alone and grid connected solar PV systems Discusses how embedded system applications with smart monitoring can control and enhance the efficiency of stand-alone and grid connected solar PV systems Explores swarm intelligence techniques for solar PV parameter estimation
Introduction to Embedded Systems, Second Edition by Edward Ashford Lee,Sanjit Arunkumar Seshia Pdf
An introduction to the engineering principles of embedded systems, with a focus on modeling, design, and analysis of cyber-physical systems. The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes. The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems.
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by Anonim Pdf
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Designing Modern Embedded Systems: Software, Hardware, and Applications by Stefan Henkler,Márcio Kreutz,Marco A. Wehrmeister,Marcelo Götz,Achim Rettberg Pdf
This book constitutes the refereed proceedings of the 7th IFIP TC 10 International Embedded Systems Symposium, IESS 2022, held in Lippstadt, Germany, during November 3-4, 2022. The 10 full revised papers and 2 short papers presented were carefully reviewed and selected from 13 submissions. The presented research and technical works cover system-level design methods, algorithms, verification and validation techniques, estimation of system properties and characteristics, performance analysis, and real-time systems design. Also, the book presents industrial and real-world application case studies that discuss the challenges and realizations of modern embedded systems, especially when it comes to including artificial intelligence algorithms and techniques in embedded systems.
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by Sudeep Pasricha,Muhammad Shafique Pdf
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Smart Embedded Systems by Arun Sinha,Abhishek Sharma,Luiz Alberto Pasini Melek,Daniele Caviglia Pdf
"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future. Salient Features: 1. The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance. 2. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications. 3. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems. 4. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologies. This book is an essential read for anyone interested in the field of embedded systems. Whether you're a student looking to broaden your knowledge base, researchers looking in-depth insights, or professionals planning to use this cutting-edge technology in real-world applications, this book offers a thorough grounding in the subject.
Embedded System Design by Lawrence J. Henschen,Julia C. Lee Pdf
Embedded systems and the Internet of Things are current major efforts in industry and will continue to be mainstream commercial activities for the foreseeable future. Embedded Systems Design presents methodologies for designing such systems and discusses major issues, both present and future, that designers must consider in bringing products with embedded processing to the market. It starts from the first step after product proposal (behavioral modelling) and carries through steps for modelling internal operations. The book discusses methods for and issues in designing safe, reliable, and robust embedded systems. It covers the selection of processors and related hardware as well as issues involved in designing the related software. Finally, the book present issues that will occur in systems designed for the Internet of Things.This book is for junior/senior/MS students in computer science, computer engineering, and electrical engineering who intend to take jobs in industry designing and implementing embedded systems and Internet of Things applications. Focuses on the design of embedded systems, starting from product conception through high-level modeling and up to the selection of hardware, software, and network platforms Discusses the trade-offs of the various techniques presented so that engineers will be able to make the best choices for designs for future products Contains a section with three chapters on making designs that are reliable, robust, and safe Includes a discussion of the two main models for the structure of the Internet of Things, as well as the issues engineers will need to take into consideration in designing future IoT applications Uses the design of a bridge control system as a continuing example across most of the chapters in order to illustrate the differences and trade-offs of the various techniques