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 Devices Embedded Systems and Industrial Applications