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Editors: Ovidiu Vermesan, SINTEF, Norway
Franz Wotawa, TU Graz, Austria
Mario Diaz Nava, STMicroelectronics, France
Björn Debaillie, imec, Belgium
Available: June 2022
The advances in industrial edge artificial intelligence (AI) are transforming the way industrial equipment and machine interact with the real world, with other machines and humans during manufacturing processes. These advances allow industrial internet of things (IIoT) and edge devices to make decisions during the manufacturing processes using sensors and actuators data.
Digital transformation is reshaping the manufacturing industry, and industrial edge AI aims to combine the potential advantages of edge computing (low latency times, reduced bandwidth, distributed architecture, improved trustworthiness, etc.) with the benefits of AI (intelligent processing, predictive solutions, classification, reasoning, etc.).
The industrial environments allow the deployment of highly distributed intelligent industrial applications in remote sites that require reliable connectivity over wireless and cellular connections. Intelligent connectivity combines IIoT, wireless/cellular and AI technologies to support new autonomous industrial applications by enabling AI capabilities at the edge and allowing manufacturing companies to improve operational efficiency and reduce risks and costs for industrial applications.
There are several critical issues to consider when bringing AI to industrial IoT applications considering training AI models at the edge, the deployment of the AI-trained inferencing models on the target reliable edge hardware platforms and the benchmarking of the solution compared with other implementations.
The next-generation trustworthy industrial AI systems offer dependability by design, transparency, explainability, verifiability, and standardised industrial solutions to be implemented into various applications across different industrial sectors.
New AI techniques like embedded machine learning (ML) and deep learning (DL) capture edge data, employ AI models and deploy them to hardware target edge devices from ultra-low-power microcontrollers to embedded devices, gateways, and on-premises servers for industrial applications. These techniques reduce latency, increase scalability, reliability, and resilience, and optimise wireless connectivity, greatly expanding IIoT capabilities.
The book overviews the latest research results and activities in industrial artificial intelligence technologies and applications based on the innovative research, developments and ideas generated by the ECSEL JU AI4DI, ANDANTE and TEMPO projects.
The authors describe industrial AI's challenges, the approaches adopted, and the main industrial systems and applications to give the reader a good insight into the technical essence of the field. The articles provide insightful material on industrial AI technologies and applications.
The book is a valuable resource for researchers, post-graduate students, practitioners, and technology developers interested in gaining insight into the industrial edge AI, IIoT, embedded machine and deep learning, new technologies, and solutions to advance the intelligent processing at the edge.
Industrial artificial intelligence, intelligent embedded technologies, edge industrial computing, neuromorphic computing, neuromorphic architecture, neural networks, benchmarking, deep learning, machine learning, industrial internet of things, image processing, semiconductor manufacturing.
PrefaceDownload as a PDF [190KB]
Benchmarking Neuromorphic Computing for Inferenceby Simon Narduzzi, Loreto Mateu, Petar Jokic, Erfan Azarkhish, and Andrea Dunbar Download as a PDF [492KB]
Benchmarking the Epiphany Processor as a Reference Neuromorphic Architectureby Maarten Molendijk, Kanishkan Vadivel, Federico Corradi, Gert-Jan van Schaik, Amirreza Yousefzadeh, and Henk Corporaal Download as a PDF [401KB]
Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Databy Preetha Vijayan, Amirreza Yousefzadeh, Manolis Sifalakis, and Rene van Leuken Download as a PDF [341KB]
An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extractionby Dinu Purice, Matthias Ludwig, and Claus Lenz Download as a PDF [7527KB]
AI Machine Vision System for Wafer Defect Detectionby Dmitry Morits, Marcelo Rizzo Piton, and Timo Laakko Download as a PDF [329KB]
Failure Detection in Silicon Packageby Saad Al-Baddai and Jan Papadoudis Download as a PDF [691KB]
S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domainby Xing Lan Liu, Eileen Salhofer, Anna Safont Andreu, and Roman Kern Download as a PDF [170KB]
Feasibility of Wafer Exchange for European Edge AI Pilot Linesby Annika Franziska Wandesleben1*, Delphine Truffier-Boutry2*, Varvara Brackmann, Benjamin Lilienthal-Uhlig, Manoj Jaysnkar, Stephan Beckx, Ivan Madarevic, Audde Demarest, Bernd Hintze, Franck Hochschulz, Yannick Le Tiec, Alessio Spessot, and Fabrice Nemouchi Download as a PDF [172KB]
A Framework for Integrating Automated Diagnosis into Simulationby David Kaufmann and Franz Wotawa Download as a PDF [829KB]
Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platformsby Simon Narduzzi, Dorvan Favre, Nuria Pazos Escudero and L. Andrea Dunbar Download as a PDF [510KB]
Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPUby Ruben Prokscha, Mathias Schneider, and Alfred Höß Download as a PDF [330KB]
Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applicationsby Ovidiu Vermesan and Marcello Coppola Download as a PDF [8547KB]
AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning Systemby Luiz Angelo Steffenel, Axel Langlet, Lilian Hollard, Lucas Mohimont, Nathalie Gaveau, Marcello Copola, Clément Pierlot, and Marine Rondeau Download as a PDF [291KB]
On the Verification of Diagnosis Modelsby Franz Wotawa and Oliver Tazl Download as a PDF [355KB]
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