Intelligent Edge-Embedded Technologies for Digitising Industry

Front Cover
Intelligent Edge-Embedded Technologies for Digitising Industry
Dedication
Acknowledgement
Contents
Preface
List of Figures
List of Tables
List of Contributors
List of Abbreviations
1 Industrial AI Technologies for Next-Generation Autonomous Operations with Sustainable Performance
	1.1 Industrial AI
		1.1.1 Challenges of Industrial AI versus Consumer AI
		1.1.2 Sustainable AI
	1.2 Capabilities Spectrum of Industrial AI
	1.3 The Industrial AI Spectrum
		1.3.1 Narrow AI vs. General AI
		1.3.2 Weak AI vs. Strong AI
		1.3.3 Basic AI vs. Super AI
		1.3.4 Red AI vs. Green AI
	1.4 AI Problem Solving Domains
		1.4.1 Expert Systems
		1.4.2 Machine Vision
		1.4.3 Robotics
		1.4.4 Biomimicry
		1.4.5 Genetic and Evolutionary Algorithms
		1.4.6 Generative AI
		1.4.7 Artificial Swarm Intelligence
		1.4.8 Natural Language Processing
		1.4.9 Machine Learning
		1.4.10 Neural Networks
		1.4.11 Automated Planning and Plan Recognition
		1.4.12 AI for the Metaverse
	1.5 Edge AI Continuum
	1.6 Symbolic AI – ML Continuum
	1.7 Logic-based AI: Knowledge Representation and Reasoning
	1.8 Hardware/Software Technology Stack
		1.8.1 ML Methods and Techniques
		1.8.2 Neural Networks Architectures
		1.8.3 Industrial Embedded AI/ML
		1.8.4 On-device ML Applications Enabling True Edge Computing
		1.8.5 Machine Learning on Embedded Devices
		1.8.6 Embedded ML Development Flow in Industrial Setting
	1.9 Summary
	References
2 Technology and Hardware for Neuromorphic Computing
	2.1 Mobile Devices Call for Efficient Neuromorphic Computing
	2.2 Neuromorphic Hardware Enables Next Generation AI
	2.3 Building Neuromorphic Hardware
		2.3.1 Approach to Realise the Emerging Technologies
		2.3.2 Approach to Derive the Hardware Architectures and Designs
		2.3.3 Approach Related to Neuromorphic Algorithms and Applications
	2.4 Positioning Within the Neuromorphic Computing Landscape
	2.5 Targeted Use Cases and Application Domains
		2.5.1 Food – Food Classification
		2.5.2 Automotive – Object Recognition and Sound Localization
		2.5.3 Digital Industry – Pattern Recognition (Keyword Spotting)
		2.5.4 Consumer – Coaching Biomechanical Assistance (Running)
		2.5.5 Medical Health – Medical Image Denoising
	2.6 Neuromorphic Hardware Technologies Being Developed
	2.7 Conclusion
	References
3 Tools and Methodologies for Training, Profiling, and Mapping a Neural Network on a Hardware Target
	3.1 Introduction
		3.1.1 Edge Computing Benefices and Challenges
		3.1.2 Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs)
	3.2 State-of-the-art of key aspects of Neural Networks
		3.2.1 ANN and SNN Hardware Aware Design
		3.2.2 Sparsity
		3.2.3 ANN-to-SNN Conversion
		3.2.4 Surrogate Gradient Descent
		3.2.5 Neural Engineering Object (Nengo) Simulator
	3.3 NN Transformation: Temporal Delta Layer
		3.3.1 Temporal Delta Layer: Training Towards Brain Inspired Temporal Sparsity for Energy Efficient Deep Neural Networks
		3.3.2 Related Works
		3.3.3 Methodology
			3.3.3.1 Delta inference
			3.3.3.2 Activation quantization to induce sparsity
			3.3.3.3 Fixed point quantization
			3.3.3.4 Learned step-size quantization
			3.3.3.5 Sparsity penalty
			3.3.3.6 Proposed algorithms
		3.3.4 Experiments and Results
			3.3.4.1 Baseline
			3.3.4.2 Experiments
			3.3.4.3 Accuracy v/s Activation sparsity
	3.4 NN Compiler for Dedicated Inference Accelerator Hardware
		3.4.1 Compiler Components
		3.4.2 ONNX Parser
		3.4.3 Hardware Architecture Representation
		3.4.4 Mapper
		3.4.5 Mapping Strategy
		3.4.6 Mapping of Deep Spiking NN Architectures to Digital SNN Inference Devices
	3.5 Simulator/Profiler
	3.6 Conclusions
		3.6.1 On NN Model Transformation
		3.6.2 On NN Compiler for Dedicated Inference Accelerator Hardware with Analog In-Memory Computing Conclusion
		3.6.3 Simulator/Profiler
	References
4 Using FeFETs as Resistive Synapses in Crossbar-based Analog MAC Accelerating Units
	4.1 Introduction and Background
	4.2 Requirements of Crossbar Structure on eNVMs
	4.3 Synapse Design
		4.3.1 Conventional Design
		4.3.2 Gate-Cascaded FeFETs
		4.3.3 Exploration Results
	4.4 Conclusion
	References
5 Emerging In-memory Computing for Neural Networks
	5.1 Memory Technologies
		5.1.1 Volatile Memories
		5.1.2 Non Volatile Memories
	5.2 In-memory Architecture
		5.2.1 Computational Domain
			5.2.1.1 Mixed signal approach
			5.2.1.2 Digital approach
		5.2.2 Target Network Quantization
			5.2.2.1 Floating point architectures
			5.2.2.2 Fixed-point architectures
			5.2.2.3 Binarized architectures
			5.2.2.4 Flexible precision architectures
	References
6 Artificial Intelligence Advancements for Digitising Industry
	6.1 AI at the Edge in Industrial Processes
	6.2 A pan-European AI Framework for Manufacturing and Process Technology
	6.3 AI Technologies
	6.4 AI Application Areas
		6.4.1 Automotive
		6.4.2 Semiconductor
		6.4.3 Industrial Machinery
		6.4.4 Food and Beverage
		6.4.5 Transportation
	6.5 AI Technology Roadmap for Digitising Industry
	6.6 Conclusion
	References
7 Impact of AI and Digital Twins on IIoT
	7.1 Introduction to the Hexa-X Project
	7.2 An Ecosystem Concept for Digital Twins in IIoT
	7.3 Digital Twins for Emergent Intelligence
	7.4 Network-aware Digital Twins for Local Insight Generation
	7.5 AI at the Intersection between DTs and HMI in Industrial IoT
	7.6 Conclusion
	References
8 Lesson Learnt and Future of AI Applied to Manufacturing
	8.1 Introduction
	8.2 IoT Enabled by Machine Learning
	8.3 Machine Learning at the Edge
		8.3.1 Applications of EdgeML in Industrial IoT
		8.3.2 Challenges in EdgeML
	8.4 Federated Learning – A Solution to Train ML Models
		8.4.1 Applications for Federated Learning in Industrial IoT
		8.4.2 Federated Learning Scenarios
		8.4.3 Challenges in Federated Learning
		8.4.4 Frameworks and products for leveraging Federated Learning
	8.5 Reducing Complexity of RX Processing
	8.6 Enhancing Reliability by Multi-Connectivity in the Uplink
	8.7 Communications in an “Embodied Artificial Intelligence” Future
	8.8 Embodied Artificial Intelligence
	8.9 High Integration as a Central Technological Driver
	8.10 Conclusion
	References
9 Ethical Considerations and Trustworthy Industrial AI Systems
	9.1 Introduction
	9.2 Ethics and Responsible AI in Industrial Environments
	9.3 Requirements for Industry-Grade AI
	9.4 Industrial AI Challenges
		9.4.1 Complexity
		9.4.2 Use of Natural Resources
		9.4.3 Pollution and Waste
		9.4.4 Energy
	9.5 Ethical Considerations for Digitising Industry
		9.5.1 AI Trustworthiness
		9.5.2 Bias and Fairness
		9.5.3 Transparency
		9.5.4 Accountability
		9.5.5 Explainability
		9.5.6 Control
		9.5.7 Human-Machine Interaction and Manipulation of Behaviour
		9.5.8 Autonomous Industrial Systems
		9.5.9 Machine Ethics
		9.5.10 Automation and Employment
	9.6 AI and the Future Digitising Industry
	9.7 Ethical Guidelines for AI in Industrial Environments
	9.8 Recommendations for Ethical AI in Industrial Environments
	9.9 Conclusion
	References
10 Current Challenges of AI Standardisation in the Digitising Industry
	10.1 Introduction
	10.2 International Principles
	10.3 Role of AI Standardisation in Digitising Industry
	10.4 Challenges Associated with AI Deployments in Industrial Environments
	10.5 AI Standardisation Needs in Industrial Automation
	10.6 Standardisation of Security and Safety in AI Systems
	10.7 The Global AI Standards Landscape and Standardisation Activities
		10.7.1 CEN-CENELEC
		10.7.2 ETSI
		10.7.3 IEC
		10.7.4 ISO
		10.7.5 IEEE
		10.7.6 IETF
		10.7.7 ITU-T
	10.8 AI Certification
	10.9 Recommendations for an AI Standardisation Roadmap
	10.10 Conclusion
	References
Index
About the Editors
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