Kooperativer Bibliotheksverbund

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Format: 1 online resource

ISBN: 9781003377382 , 1003377386 , 9781000852035 , 1000852032 , 9781000852059 , 1000852059

Content: 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 solutionscan 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 inhardware 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 bookprovides an overview ofthe latest research results and activities in industrialAI 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 technicalnature of this field. Thechapters 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.

Additional Edition: Print version: ISBN 9788770227919

Language: English

DOI: 10.1201/9781003377382

URL: https://www.taylorfrancis.com/books/9781003377382

URL: kostenfrei

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 online resource (242 pages)

Edition: 1st ed.

ISBN: 1-000-85203-2 , 1-00-337738-6 , 1-000-85205-9 , 1-003-37738-6

Content: This 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.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Acknowledgement -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- Chapter 1: Benchmarking Neuromorphic Computing for Inference -- 1.1: Introduction -- 1.2: State of the art in Benchmarking -- 1.2.1: Machine Learning -- 1.2.2: Hardware -- 1.3: Guidelines -- 1.3.1: Fair and Unfair Benchmarking -- 1.3.2: Combined KPIs and Approaches for Benchmarking -- 1.3.3: Outlook : Use-case Based Benchmarking -- 1.4: Conclusion -- References -- Chapter 2: Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture -- 2.1: Introduction and Background -- 2.2: Comparison with a Few Well-Known Digital Neuromorphic Platforms -- 2.3: Major Challenges in Neuromorphic Architectures -- 2.3.1: Memory Allocation -- 2.3.2: Efficient Communication -- 2.3.3: Mapping SNN onto Hardware -- 2.3.4: On-chip Learning -- 2.3.5: Idle Power Consumption -- 2.4: Measurements from Epiphany -- 2.5: Conclusion -- References -- Chapter 3: Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data -- 3.1: Introduction -- 3.2: Related Works -- 3.3: Methodology -- 3.3.1: Delta Inference -- 3.3.2: Sparsity Induction Using Activation Quantization -- 3.3.2.1: Fixed Point Quantization -- 3.3.2.2: Learned Step-Size Quantization -- 3.3.3: Sparsity Penalty -- 3.4: Experiments and Results -- 3.4.1: Baseline -- 3.4.2: Experiments -- 3.4.3: Result Analysis -- 3.5: Conclusion -- References -- Chapter 4: An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction -- 4.1: Introduction -- 4.2: Semantic Segmentation -- 4.2.1: Proof of Concept and Architecture Overview -- 4.2.2: Implementation Details and Result Overview -- 4.3: Parameter Extraction -- 4.4: Conclusion -- 4.5: Future Work -- References. , Chapter 5: AI Machine Vision System for Wafer Defect Detection -- 5.1: Introduction and Background -- 5.2: Machine Vision-based System Description -- 5.3: Conclusion -- References -- Chapter 6: Failure Detection in Silicon Package -- 6.1: Introduction and Background -- 6.2: Dataset Description -- 6.2.1: Data Collection & -- Labelling -- 6.3: Development and Deployment -- 6.4: Transfer Learning and Scalability -- 6.5: Result and Discussion -- 6.6: Conclusion and Outlooks -- References -- Chapter 7: S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain -- 7.1: Introduction -- 7.2: Dataset Creation -- 7.2.1: Corpus -- 7.2.2: Annotation Guideline -- 7.2.3: Annotation Methodology -- 7.2.4: Dataset Statistics -- 7.2.5: Causal Cue Phrases -- 7.3: Baseline Performance -- 7.3.1: Train-Test Split -- 7.3.2: Causal Argument Extraction -- 7.3.3: Error Analysis -- 7.4: Conclusions -- References -- Chapter 8: Feasibility of Wafer Exchange for European Edge AI Pilot Lines -- 8.1: Introduction -- 8.2: Technical Details and Comparison -- 8.2.1: Comparison TXRF and VPD-ICPMS Equipment for Surface Analysis -- 8.2.2: VPD-ICPMS Analyses on Bevel -- 8.3: Cross-Contamination Check-Investigation -- 8.3.1: Example for the Comparison of the Institutes -- 8.4: Conclusiion -- References -- Chapter 9: A Framework for Integrating Automated Diagnosis into Simulation -- 9.1: Introduction -- 9.2: Model-based Diagnosis -- 9.3: Simulation and Diagnosis Framework -- 9.3.1: FMU Simulation Tool -- 9.3.2: ASP Diagnose Tool -- 9.4: Experiment -- 9.5: Conclusion -- References -- Chapter 10: Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms -- 10.1: Introduction -- 10.2: Related Work -- 10.3: Methods -- 10.3.1: Neural Network Deployment -- 10.3.1.1: Task and Model -- 10.3.1.2: Experimental Setup -- 10.3.1.3: Deployment. , 10.3.2: Measuring the Ease of Deployment -- 10.4: Results -- 10.4.1: Inference Results -- 10.4.2: Perceived Effort -- 10.5: Conclusion -- References -- Chapter 11: Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU -- 11.1: Introduction -- 11.2: Related Work -- 11.3: Experimental Setup -- 11.3.1: Google Coral Edge TPU -- 11.3.2: YOLOv5 -- 11.4: Performance Considerations -- 11.4.1: Graph Optimization -- 11.4.1.1: Incompatible Operations -- 11.4.1.2: Tensor Transformations -- 11.4.2: Performance Evaluation -- 11.4.2.1: Speed-Accuracy Comparison -- 11.4.2.2: USB Speed Comparison -- 11.4.3: Deployment Pipeline -- 11.5: Conclusion and Future Work -- References -- Chapter 12: Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications -- 12.1: Introduction and Background -- 12.2: Machine and Deep Learning for Embedded Edge Predictive Maintenance -- 12.3: Approaches for Predictive Maintenance -- 12.3.1: Hardware and Software Platforms -- 12.3.2: Motor Classification Use Case -- 12.4: Experimental Setup -- 12.4.1: Signal Data Acquisition and Pre-processing -- 12.4.2: Feature Extraction, ML/DL Model Selection and Training -- 12.4.3: Optimisation and Tuning Performance -- 12.4.4: Testing -- 12.4.5: Deployment -- 12.4.6: Inference -- 12.5: Discussion and Future Work -- References -- Chapter 13: AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System -- 13.1: Introduction -- 13.2: Research Material and Methodology -- 13.2.1: Datasets -- 13.2.2: Labelling Methodology -- 13.3: Machine Learning Models -- 13.4: Results -- 13.4.1: Primary Mildew Infection Alerts -- 13.4.2: Secondary Mildew Infection Alerts -- 13.5: Discussion -- 13.6: Conclusion -- References -- Chapter 14: On the Verification of Diagnosis Models -- 14.1: Introduction -- 14.2: The Model Testing Challenge -- 14.3: Use Case. , 14.4: Open Issues and Challenges -- 14.5: Conclusion -- References -- Index -- About the Editors.

Additional Edition: ISBN 9788770227919

Language: English

URL: lizenzpflichtig

UID:

Format: 1 online resource (242 pages)

Edition: 1st ed.

ISBN: 9781000852059

Content: This 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.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Acknowledgement -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- Chapter 1: Benchmarking Neuromorphic Computing for Inference -- 1.1: Introduction -- 1.2: State of the art in Benchmarking -- 1.2.1: Machine Learning -- 1.2.2: Hardware -- 1.3: Guidelines -- 1.3.1: Fair and Unfair Benchmarking -- 1.3.2: Combined KPIs and Approaches for Benchmarking -- 1.3.3: Outlook : Use-case Based Benchmarking -- 1.4: Conclusion -- References -- Chapter 2: Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture -- 2.1: Introduction and Background -- 2.2: Comparison with a Few Well-Known Digital Neuromorphic Platforms -- 2.3: Major Challenges in Neuromorphic Architectures -- 2.3.1: Memory Allocation -- 2.3.2: Efficient Communication -- 2.3.3: Mapping SNN onto Hardware -- 2.3.4: On-chip Learning -- 2.3.5: Idle Power Consumption -- 2.4: Measurements from Epiphany -- 2.5: Conclusion -- References -- Chapter 3: Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data -- 3.1: Introduction -- 3.2: Related Works -- 3.3: Methodology -- 3.3.1: Delta Inference -- 3.3.2: Sparsity Induction Using Activation Quantization -- 3.3.2.1: Fixed Point Quantization -- 3.3.2.2: Learned Step-Size Quantization -- 3.3.3: Sparsity Penalty -- 3.4: Experiments and Results -- 3.4.1: Baseline -- 3.4.2: Experiments -- 3.4.3: Result Analysis -- 3.5: Conclusion -- References -- Chapter 4: An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction -- 4.1: Introduction -- 4.2: Semantic Segmentation -- 4.2.1: Proof of Concept and Architecture Overview -- 4.2.2: Implementation Details and Result Overview -- 4.3: Parameter Extraction -- 4.4: Conclusion -- 4.5: Future Work -- References. , Chapter 5: AI Machine Vision System for Wafer Defect Detection -- 5.1: Introduction and Background -- 5.2: Machine Vision-based System Description -- 5.3: Conclusion -- References -- Chapter 6: Failure Detection in Silicon Package -- 6.1: Introduction and Background -- 6.2: Dataset Description -- 6.2.1: Data Collection & -- Labelling -- 6.3: Development and Deployment -- 6.4: Transfer Learning and Scalability -- 6.5: Result and Discussion -- 6.6: Conclusion and Outlooks -- References -- Chapter 7: S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain -- 7.1: Introduction -- 7.2: Dataset Creation -- 7.2.1: Corpus -- 7.2.2: Annotation Guideline -- 7.2.3: Annotation Methodology -- 7.2.4: Dataset Statistics -- 7.2.5: Causal Cue Phrases -- 7.3: Baseline Performance -- 7.3.1: Train-Test Split -- 7.3.2: Causal Argument Extraction -- 7.3.3: Error Analysis -- 7.4: Conclusions -- References -- Chapter 8: Feasibility of Wafer Exchange for European Edge AI Pilot Lines -- 8.1: Introduction -- 8.2: Technical Details and Comparison -- 8.2.1: Comparison TXRF and VPD-ICPMS Equipment for Surface Analysis -- 8.2.2: VPD-ICPMS Analyses on Bevel -- 8.3: Cross-Contamination Check-Investigation -- 8.3.1: Example for the Comparison of the Institutes -- 8.4: Conclusiion -- References -- Chapter 9: A Framework for Integrating Automated Diagnosis into Simulation -- 9.1: Introduction -- 9.2: Model-based Diagnosis -- 9.3: Simulation and Diagnosis Framework -- 9.3.1: FMU Simulation Tool -- 9.3.2: ASP Diagnose Tool -- 9.4: Experiment -- 9.5: Conclusion -- References -- Chapter 10: Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms -- 10.1: Introduction -- 10.2: Related Work -- 10.3: Methods -- 10.3.1: Neural Network Deployment -- 10.3.1.1: Task and Model -- 10.3.1.2: Experimental Setup -- 10.3.1.3: Deployment. , 10.3.2: Measuring the Ease of Deployment -- 10.4: Results -- 10.4.1: Inference Results -- 10.4.2: Perceived Effort -- 10.5: Conclusion -- References -- Chapter 11: Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU -- 11.1: Introduction -- 11.2: Related Work -- 11.3: Experimental Setup -- 11.3.1: Google Coral Edge TPU -- 11.3.2: YOLOv5 -- 11.4: Performance Considerations -- 11.4.1: Graph Optimization -- 11.4.1.1: Incompatible Operations -- 11.4.1.2: Tensor Transformations -- 11.4.2: Performance Evaluation -- 11.4.2.1: Speed-Accuracy Comparison -- 11.4.2.2: USB Speed Comparison -- 11.4.3: Deployment Pipeline -- 11.5: Conclusion and Future Work -- References -- Chapter 12: Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications -- 12.1: Introduction and Background -- 12.2: Machine and Deep Learning for Embedded Edge Predictive Maintenance -- 12.3: Approaches for Predictive Maintenance -- 12.3.1: Hardware and Software Platforms -- 12.3.2: Motor Classification Use Case -- 12.4: Experimental Setup -- 12.4.1: Signal Data Acquisition and Pre-processing -- 12.4.2: Feature Extraction, ML/DL Model Selection and Training -- 12.4.3: Optimisation and Tuning Performance -- 12.4.4: Testing -- 12.4.5: Deployment -- 12.4.6: Inference -- 12.5: Discussion and Future Work -- References -- Chapter 13: AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System -- 13.1: Introduction -- 13.2: Research Material and Methodology -- 13.2.1: Datasets -- 13.2.2: Labelling Methodology -- 13.3: Machine Learning Models -- 13.4: Results -- 13.4.1: Primary Mildew Infection Alerts -- 13.4.2: Secondary Mildew Infection Alerts -- 13.5: Discussion -- 13.6: Conclusion -- References -- Chapter 14: On the Verification of Diagnosis Models -- 14.1: Introduction -- 14.2: The Model Testing Challenge -- 14.3: Use Case. , 14.4: Open Issues and Challenges -- 14.5: Conclusion -- References -- Index -- About the Editors.

Additional Edition: Print version: Vermesan, Ovidiu Industrial Artificial Intelligence Technologies and Applications Milton : River Publishers,c2023 ISBN 9788770227919

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource (242 pages)

Edition: 1st ed.

ISBN: 1003377386 , 9781003377382 , 1000852059 , 9781000852059

Content: This 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.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Acknowledgement -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- Chapter 1: Benchmarking Neuromorphic Computing for Inference -- 1.1: Introduction -- 1.2: State of the art in Benchmarking -- 1.2.1: Machine Learning -- 1.2.2: Hardware -- 1.3: Guidelines -- 1.3.1: Fair and Unfair Benchmarking -- 1.3.2: Combined KPIs and Approaches for Benchmarking -- 1.3.3: Outlook : Use-case Based Benchmarking -- 1.4: Conclusion -- References -- Chapter 2: Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture -- 2.1: Introduction and Background -- 2.2: Comparison with a Few Well-Known Digital Neuromorphic Platforms -- 2.3: Major Challenges in Neuromorphic Architectures -- 2.3.1: Memory Allocation -- 2.3.2: Efficient Communication -- 2.3.3: Mapping SNN onto Hardware -- 2.3.4: On-chip Learning -- 2.3.5: Idle Power Consumption -- 2.4: Measurements from Epiphany -- 2.5: Conclusion -- References -- Chapter 3: Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data -- 3.1: Introduction -- 3.2: Related Works -- 3.3: Methodology -- 3.3.1: Delta Inference -- 3.3.2: Sparsity Induction Using Activation Quantization -- 3.3.2.1: Fixed Point Quantization -- 3.3.2.2: Learned Step-Size Quantization -- 3.3.3: Sparsity Penalty -- 3.4: Experiments and Results -- 3.4.1: Baseline -- 3.4.2: Experiments -- 3.4.3: Result Analysis -- 3.5: Conclusion -- References -- Chapter 4: An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction -- 4.1: Introduction -- 4.2: Semantic Segmentation -- 4.2.1: Proof of Concept and Architecture Overview -- 4.2.2: Implementation Details and Result Overview -- 4.3: Parameter Extraction -- 4.4: Conclusion -- 4.5: Future Work -- References. , Chapter 5: AI Machine Vision System for Wafer Defect Detection -- 5.1: Introduction and Background -- 5.2: Machine Vision-based System Description -- 5.3: Conclusion -- References -- Chapter 6: Failure Detection in Silicon Package -- 6.1: Introduction and Background -- 6.2: Dataset Description -- 6.2.1: Data Collection & -- Labelling -- 6.3: Development and Deployment -- 6.4: Transfer Learning and Scalability -- 6.5: Result and Discussion -- 6.6: Conclusion and Outlooks -- References -- Chapter 7: S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain -- 7.1: Introduction -- 7.2: Dataset Creation -- 7.2.1: Corpus -- 7.2.2: Annotation Guideline -- 7.2.3: Annotation Methodology -- 7.2.4: Dataset Statistics -- 7.2.5: Causal Cue Phrases -- 7.3: Baseline Performance -- 7.3.1: Train-Test Split -- 7.3.2: Causal Argument Extraction -- 7.3.3: Error Analysis -- 7.4: Conclusions -- References -- Chapter 8: Feasibility of Wafer Exchange for European Edge AI Pilot Lines -- 8.1: Introduction -- 8.2: Technical Details and Comparison -- 8.2.1: Comparison TXRF and VPD-ICPMS Equipment for Surface Analysis -- 8.2.2: VPD-ICPMS Analyses on Bevel -- 8.3: Cross-Contamination Check-Investigation -- 8.3.1: Example for the Comparison of the Institutes -- 8.4: Conclusiion -- References -- Chapter 9: A Framework for Integrating Automated Diagnosis into Simulation -- 9.1: Introduction -- 9.2: Model-based Diagnosis -- 9.3: Simulation and Diagnosis Framework -- 9.3.1: FMU Simulation Tool -- 9.3.2: ASP Diagnose Tool -- 9.4: Experiment -- 9.5: Conclusion -- References -- Chapter 10: Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms -- 10.1: Introduction -- 10.2: Related Work -- 10.3: Methods -- 10.3.1: Neural Network Deployment -- 10.3.1.1: Task and Model -- 10.3.1.2: Experimental Setup -- 10.3.1.3: Deployment. , 10.3.2: Measuring the Ease of Deployment -- 10.4: Results -- 10.4.1: Inference Results -- 10.4.2: Perceived Effort -- 10.5: Conclusion -- References -- Chapter 11: Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU -- 11.1: Introduction -- 11.2: Related Work -- 11.3: Experimental Setup -- 11.3.1: Google Coral Edge TPU -- 11.3.2: YOLOv5 -- 11.4: Performance Considerations -- 11.4.1: Graph Optimization -- 11.4.1.1: Incompatible Operations -- 11.4.1.2: Tensor Transformations -- 11.4.2: Performance Evaluation -- 11.4.2.1: Speed-Accuracy Comparison -- 11.4.2.2: USB Speed Comparison -- 11.4.3: Deployment Pipeline -- 11.5: Conclusion and Future Work -- References -- Chapter 12: Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications -- 12.1: Introduction and Background -- 12.2: Machine and Deep Learning for Embedded Edge Predictive Maintenance -- 12.3: Approaches for Predictive Maintenance -- 12.3.1: Hardware and Software Platforms -- 12.3.2: Motor Classification Use Case -- 12.4: Experimental Setup -- 12.4.1: Signal Data Acquisition and Pre-processing -- 12.4.2: Feature Extraction, ML/DL Model Selection and Training -- 12.4.3: Optimisation and Tuning Performance -- 12.4.4: Testing -- 12.4.5: Deployment -- 12.4.6: Inference -- 12.5: Discussion and Future Work -- References -- Chapter 13: AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System -- 13.1: Introduction -- 13.2: Research Material and Methodology -- 13.2.1: Datasets -- 13.2.2: Labelling Methodology -- 13.3: Machine Learning Models -- 13.4: Results -- 13.4.1: Primary Mildew Infection Alerts -- 13.4.2: Secondary Mildew Infection Alerts -- 13.5: Discussion -- 13.6: Conclusion -- References -- Chapter 14: On the Verification of Diagnosis Models -- 14.1: Introduction -- 14.2: The Model Testing Challenge -- 14.3: Use Case. , 14.4: Open Issues and Challenges -- 14.5: Conclusion -- References -- Index -- About the Editors.

Additional Edition: ISBN 9788770227919

Language: English

Keywords: Electronic books.

URL: Click here to view book

UID:

Format: 1 online resource (242 pages)

Edition: 1st ed.

ISBN: 1-000-85203-2 , 1-00-337738-6 , 1-000-85205-9 , 1-003-37738-6

Content: This 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.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Acknowledgement -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- Chapter 1: Benchmarking Neuromorphic Computing for Inference -- 1.1: Introduction -- 1.2: State of the art in Benchmarking -- 1.2.1: Machine Learning -- 1.2.2: Hardware -- 1.3: Guidelines -- 1.3.1: Fair and Unfair Benchmarking -- 1.3.2: Combined KPIs and Approaches for Benchmarking -- 1.3.3: Outlook : Use-case Based Benchmarking -- 1.4: Conclusion -- References -- Chapter 2: Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture -- 2.1: Introduction and Background -- 2.2: Comparison with a Few Well-Known Digital Neuromorphic Platforms -- 2.3: Major Challenges in Neuromorphic Architectures -- 2.3.1: Memory Allocation -- 2.3.2: Efficient Communication -- 2.3.3: Mapping SNN onto Hardware -- 2.3.4: On-chip Learning -- 2.3.5: Idle Power Consumption -- 2.4: Measurements from Epiphany -- 2.5: Conclusion -- References -- Chapter 3: Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data -- 3.1: Introduction -- 3.2: Related Works -- 3.3: Methodology -- 3.3.1: Delta Inference -- 3.3.2: Sparsity Induction Using Activation Quantization -- 3.3.2.1: Fixed Point Quantization -- 3.3.2.2: Learned Step-Size Quantization -- 3.3.3: Sparsity Penalty -- 3.4: Experiments and Results -- 3.4.1: Baseline -- 3.4.2: Experiments -- 3.4.3: Result Analysis -- 3.5: Conclusion -- References -- Chapter 4: An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction -- 4.1: Introduction -- 4.2: Semantic Segmentation -- 4.2.1: Proof of Concept and Architecture Overview -- 4.2.2: Implementation Details and Result Overview -- 4.3: Parameter Extraction -- 4.4: Conclusion -- 4.5: Future Work -- References. , Chapter 5: AI Machine Vision System for Wafer Defect Detection -- 5.1: Introduction and Background -- 5.2: Machine Vision-based System Description -- 5.3: Conclusion -- References -- Chapter 6: Failure Detection in Silicon Package -- 6.1: Introduction and Background -- 6.2: Dataset Description -- 6.2.1: Data Collection & -- Labelling -- 6.3: Development and Deployment -- 6.4: Transfer Learning and Scalability -- 6.5: Result and Discussion -- 6.6: Conclusion and Outlooks -- References -- Chapter 7: S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain -- 7.1: Introduction -- 7.2: Dataset Creation -- 7.2.1: Corpus -- 7.2.2: Annotation Guideline -- 7.2.3: Annotation Methodology -- 7.2.4: Dataset Statistics -- 7.2.5: Causal Cue Phrases -- 7.3: Baseline Performance -- 7.3.1: Train-Test Split -- 7.3.2: Causal Argument Extraction -- 7.3.3: Error Analysis -- 7.4: Conclusions -- References -- Chapter 8: Feasibility of Wafer Exchange for European Edge AI Pilot Lines -- 8.1: Introduction -- 8.2: Technical Details and Comparison -- 8.2.1: Comparison TXRF and VPD-ICPMS Equipment for Surface Analysis -- 8.2.2: VPD-ICPMS Analyses on Bevel -- 8.3: Cross-Contamination Check-Investigation -- 8.3.1: Example for the Comparison of the Institutes -- 8.4: Conclusiion -- References -- Chapter 9: A Framework for Integrating Automated Diagnosis into Simulation -- 9.1: Introduction -- 9.2: Model-based Diagnosis -- 9.3: Simulation and Diagnosis Framework -- 9.3.1: FMU Simulation Tool -- 9.3.2: ASP Diagnose Tool -- 9.4: Experiment -- 9.5: Conclusion -- References -- Chapter 10: Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms -- 10.1: Introduction -- 10.2: Related Work -- 10.3: Methods -- 10.3.1: Neural Network Deployment -- 10.3.1.1: Task and Model -- 10.3.1.2: Experimental Setup -- 10.3.1.3: Deployment. , 10.3.2: Measuring the Ease of Deployment -- 10.4: Results -- 10.4.1: Inference Results -- 10.4.2: Perceived Effort -- 10.5: Conclusion -- References -- Chapter 11: Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU -- 11.1: Introduction -- 11.2: Related Work -- 11.3: Experimental Setup -- 11.3.1: Google Coral Edge TPU -- 11.3.2: YOLOv5 -- 11.4: Performance Considerations -- 11.4.1: Graph Optimization -- 11.4.1.1: Incompatible Operations -- 11.4.1.2: Tensor Transformations -- 11.4.2: Performance Evaluation -- 11.4.2.1: Speed-Accuracy Comparison -- 11.4.2.2: USB Speed Comparison -- 11.4.3: Deployment Pipeline -- 11.5: Conclusion and Future Work -- References -- Chapter 12: Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications -- 12.1: Introduction and Background -- 12.2: Machine and Deep Learning for Embedded Edge Predictive Maintenance -- 12.3: Approaches for Predictive Maintenance -- 12.3.1: Hardware and Software Platforms -- 12.3.2: Motor Classification Use Case -- 12.4: Experimental Setup -- 12.4.1: Signal Data Acquisition and Pre-processing -- 12.4.2: Feature Extraction, ML/DL Model Selection and Training -- 12.4.3: Optimisation and Tuning Performance -- 12.4.4: Testing -- 12.4.5: Deployment -- 12.4.6: Inference -- 12.5: Discussion and Future Work -- References -- Chapter 13: AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System -- 13.1: Introduction -- 13.2: Research Material and Methodology -- 13.2.1: Datasets -- 13.2.2: Labelling Methodology -- 13.3: Machine Learning Models -- 13.4: Results -- 13.4.1: Primary Mildew Infection Alerts -- 13.4.2: Secondary Mildew Infection Alerts -- 13.5: Discussion -- 13.6: Conclusion -- References -- Chapter 14: On the Verification of Diagnosis Models -- 14.1: Introduction -- 14.2: The Model Testing Challenge -- 14.3: Use Case. , 14.4: Open Issues and Challenges -- 14.5: Conclusion -- References -- Index -- About the Editors.

Additional Edition: ISBN 9788770227919

Language: English

UID:

Format: 1 online resource (242 pages)

Edition: 1st ed.

ISBN: 1-000-85203-2 , 1-00-337738-6 , 1-000-85205-9 , 1-003-37738-6

Content: This 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.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Acknowledgement -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- Chapter 1: Benchmarking Neuromorphic Computing for Inference -- 1.1: Introduction -- 1.2: State of the art in Benchmarking -- 1.2.1: Machine Learning -- 1.2.2: Hardware -- 1.3: Guidelines -- 1.3.1: Fair and Unfair Benchmarking -- 1.3.2: Combined KPIs and Approaches for Benchmarking -- 1.3.3: Outlook : Use-case Based Benchmarking -- 1.4: Conclusion -- References -- Chapter 2: Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture -- 2.1: Introduction and Background -- 2.2: Comparison with a Few Well-Known Digital Neuromorphic Platforms -- 2.3: Major Challenges in Neuromorphic Architectures -- 2.3.1: Memory Allocation -- 2.3.2: Efficient Communication -- 2.3.3: Mapping SNN onto Hardware -- 2.3.4: On-chip Learning -- 2.3.5: Idle Power Consumption -- 2.4: Measurements from Epiphany -- 2.5: Conclusion -- References -- Chapter 3: Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data -- 3.1: Introduction -- 3.2: Related Works -- 3.3: Methodology -- 3.3.1: Delta Inference -- 3.3.2: Sparsity Induction Using Activation Quantization -- 3.3.2.1: Fixed Point Quantization -- 3.3.2.2: Learned Step-Size Quantization -- 3.3.3: Sparsity Penalty -- 3.4: Experiments and Results -- 3.4.1: Baseline -- 3.4.2: Experiments -- 3.4.3: Result Analysis -- 3.5: Conclusion -- References -- Chapter 4: An End-to-End AI-based Automated Process for Semiconductor Device Parameter Extraction -- 4.1: Introduction -- 4.2: Semantic Segmentation -- 4.2.1: Proof of Concept and Architecture Overview -- 4.2.2: Implementation Details and Result Overview -- 4.3: Parameter Extraction -- 4.4: Conclusion -- 4.5: Future Work -- References. , Chapter 5: AI Machine Vision System for Wafer Defect Detection -- 5.1: Introduction and Background -- 5.2: Machine Vision-based System Description -- 5.3: Conclusion -- References -- Chapter 6: Failure Detection in Silicon Package -- 6.1: Introduction and Background -- 6.2: Dataset Description -- 6.2.1: Data Collection & -- Labelling -- 6.3: Development and Deployment -- 6.4: Transfer Learning and Scalability -- 6.5: Result and Discussion -- 6.6: Conclusion and Outlooks -- References -- Chapter 7: S2ORC-SemiCause: Annotating and Analysing Causality in the Semiconductor Domain -- 7.1: Introduction -- 7.2: Dataset Creation -- 7.2.1: Corpus -- 7.2.2: Annotation Guideline -- 7.2.3: Annotation Methodology -- 7.2.4: Dataset Statistics -- 7.2.5: Causal Cue Phrases -- 7.3: Baseline Performance -- 7.3.1: Train-Test Split -- 7.3.2: Causal Argument Extraction -- 7.3.3: Error Analysis -- 7.4: Conclusions -- References -- Chapter 8: Feasibility of Wafer Exchange for European Edge AI Pilot Lines -- 8.1: Introduction -- 8.2: Technical Details and Comparison -- 8.2.1: Comparison TXRF and VPD-ICPMS Equipment for Surface Analysis -- 8.2.2: VPD-ICPMS Analyses on Bevel -- 8.3: Cross-Contamination Check-Investigation -- 8.3.1: Example for the Comparison of the Institutes -- 8.4: Conclusiion -- References -- Chapter 9: A Framework for Integrating Automated Diagnosis into Simulation -- 9.1: Introduction -- 9.2: Model-based Diagnosis -- 9.3: Simulation and Diagnosis Framework -- 9.3.1: FMU Simulation Tool -- 9.3.2: ASP Diagnose Tool -- 9.4: Experiment -- 9.5: Conclusion -- References -- Chapter 10: Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms -- 10.1: Introduction -- 10.2: Related Work -- 10.3: Methods -- 10.3.1: Neural Network Deployment -- 10.3.1.1: Task and Model -- 10.3.1.2: Experimental Setup -- 10.3.1.3: Deployment. , 10.3.2: Measuring the Ease of Deployment -- 10.4: Results -- 10.4.1: Inference Results -- 10.4.2: Perceived Effort -- 10.5: Conclusion -- References -- Chapter 11: Efficient Edge Deployment Demonstrated on YOLOv5 and Coral Edge TPU -- 11.1: Introduction -- 11.2: Related Work -- 11.3: Experimental Setup -- 11.3.1: Google Coral Edge TPU -- 11.3.2: YOLOv5 -- 11.4: Performance Considerations -- 11.4.1: Graph Optimization -- 11.4.1.1: Incompatible Operations -- 11.4.1.2: Tensor Transformations -- 11.4.2: Performance Evaluation -- 11.4.2.1: Speed-Accuracy Comparison -- 11.4.2.2: USB Speed Comparison -- 11.4.3: Deployment Pipeline -- 11.5: Conclusion and Future Work -- References -- Chapter 12: Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications -- 12.1: Introduction and Background -- 12.2: Machine and Deep Learning for Embedded Edge Predictive Maintenance -- 12.3: Approaches for Predictive Maintenance -- 12.3.1: Hardware and Software Platforms -- 12.3.2: Motor Classification Use Case -- 12.4: Experimental Setup -- 12.4.1: Signal Data Acquisition and Pre-processing -- 12.4.2: Feature Extraction, ML/DL Model Selection and Training -- 12.4.3: Optimisation and Tuning Performance -- 12.4.4: Testing -- 12.4.5: Deployment -- 12.4.6: Inference -- 12.5: Discussion and Future Work -- References -- Chapter 13: AI-Driven Strategies to Implement a Grapevine Downy Mildew Warning System -- 13.1: Introduction -- 13.2: Research Material and Methodology -- 13.2.1: Datasets -- 13.2.2: Labelling Methodology -- 13.3: Machine Learning Models -- 13.4: Results -- 13.4.1: Primary Mildew Infection Alerts -- 13.4.2: Secondary Mildew Infection Alerts -- 13.5: Discussion -- 13.6: Conclusion -- References -- Chapter 14: On the Verification of Diagnosis Models -- 14.1: Introduction -- 14.2: The Model Testing Challenge -- 14.3: Use Case. , 14.4: Open Issues and Challenges -- 14.5: Conclusion -- References -- Index -- About the Editors.

Additional Edition: ISBN 9788770227919

Language: English