Front Cover
Industrial Artificial Intelligence Technologies and Applications
Dedication
Acknowledgement
Contents
Preface
List of Figures
List of Tables
List of Contributors
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
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
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
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
5 AI Machine Vision System forWafer Defect Detection
	5.1 Introduction and Background
	5.2 Machine Vision-based System Description
	5.3 Conclusion
	References
6 Failure Detection in Silicon Package
	6.1 Introduction and Background
	6.2 Dataset Description
		6.2.1 Data Collection and Labelling
	6.3 Development and Deployment
	6.4 Transfer Learning and Scalability
	6.5 Result and Discussion
	6.6 Conclusion and Outlooks
	References
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
8 Feasibility ofWafer 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
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
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
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
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
13 AI-Driven Strategies to Implement a Grapevine Downy MildewWarning 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
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
Back Cover