Embedded Artificial Intelligence
Download Embedded Artificial Intelligence PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Embedded Artificial Intelligence book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Embedded Artificial Intelligence
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.
Embedded Artificial Intelligence
This book explores the role of embedded AI in revolutionizing industries such as healthcare, transportation, manufacturing, and retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of this book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimizing algorithms and models for embedded systems, including hardware acceleration, model compression, and quantization. Key features: • Explores security experiments in emerging post‐CMOS technologies using AI, including side channel attack‐resistant embedded systems. • Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems. • Considers ethical and societal implications of embedded AI vis‐a‐vis the need for responsible development and deployment of embedded AI systems. • Focuses on application‐based research and case studies to develop embedded AI systems for real‐life applications. • Examines high‐end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power efficiency. This reference book is for students, researchers, and professionals interested in embedded AI and relevant branches of computer science, electrical engineering, or artificial intelligence.
Embedded Artificial Intelligence
"This book explores the role of Embedded AI in revolutionising industries such as healthcare, transportation, manufacturing, retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of the book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimising algorithms and models for embedded systems, including hardware acceleration, model compression, and quantisation. Explores security experiments in emerging post-CMOS technologies using AI, including side-channel attack-resistant embedded systems Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems Considers ethical and societal implications of embedded AI vis-a-vis the need for responsible development and deployment of embedded AI systems Focuses on application-based research and case studies to develop embedded AI systems for real-life applications Examines high-end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power-efficiency This reference book is for students, researchers and professionals interested in Embedded AI, and relevant branches of computer science, electrical engineering, or artificial intelligence"--