The advances in industrial edge artificial intelligence (AI) are transforming the way industrial equipment and machines 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.
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 introducing AI to industrial IoT applications considering training AI models at the edge, the deployment of the AI-trained inferencing models on the target edge hardware platforms, and the benchmarking of solutions compared to other implementations.
Next-generation trustworthy industrial AI systems offer dependability in terms of their design, transparency, explainability, verifiability, and standardised industrial solutions can be implemented in various applications across different industrial sectors.
New AI techniques such as embedded machine learning (ML) and deep learning (DL), capture edge data, employ AI models, and deploy these in 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 the capabilities of the IIoT.
This book provides an overview of the latest research results and activities in industrial AI 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 extensive insight into the technical nature of this field. The chapters provide insightful material on industrial AI technologies and applications.
This book is a valuable resource for researchers, post-graduate students, practitioners, and technoloyg developers interested in gaining insight into industrial edge AI, the IIoT, embedded machine and deep learning, new technologies, and solutions to advance intelligent processing at the edge.
The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non-Commercial (CC-BY-NC) 4.0 International License.
1. Benchmarking Neuromorphic Computing for Inference
2. Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture
3. Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data
4. An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction
5. AI Machine Vision System for Wafer Defect Detection
6. Failure Detection in Silicon Package
7. S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain
8. Feasibility of Wafer Exchange for European Edge AI Pilot Lines
9. A Framework for Integrating Automated Diagnosis into Simulation
10. Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms
11. Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU
12. Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications
13. AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System
14. On the Verification of Diagnosis Models