Performance Evaluation of AI Algorithms on Heterogeneous Edge Devices for Manufacturing

Keyphrases

• Performance Evaluation 100%
• Manufacturing Domain 100%
• Artificial Intelligence Algorithm 100%
• Heterogeneous Edge Devices 100%
• Edge Devices 66%
• Support Vector Machine 33%
• Computational Resources 33%
• Energy Consumption 33%
• Random Forest 33%
• Trial-and-error 33%
• CPU Utilization 33%
• Execution Time 33%
• Artificial Intelligence Techniques 33%
• Matrix Inversion 33%
• Network Bandwidth 33%
• Density-based Spatial Clustering of Applications with Noise (DBSCAN) 33%
• Further Processing 33%
• Memory Consumption 33%
• Cyber-physical Production Systems 33%
• Real-time Requirements 33%
• Smart Manufacturing 33%
• Memory Time 33%
• Edge Computing 33%
• Butterworth Filter 33%
• Hardware Devices 33%
• Algorithm Benchmarking 33%
• Single Board Computer 33%
• Heterogeneous Hardware 33%
• Grubbs Test 33%
• Matrix multiplication 33%
• Process Solutions 33%
• Algorithm Behaviour 33%

Computer Science

• Performance Evaluation 100%
• Artificial Intelligence 100%
• Support Vector Machine 33%
• Random Decision Forest 33%
• Execution Time 33%
• Benchmarking 33%
• Time Complexity 33%
• Production System 33%
• Time Requirement 33%
• Network Bandwidth 33%
• Suitable Hardware 33%
• Edge Computing 33%
• Butterworth Filter 33%
• Memory Consumption 33%
• Matrix Multiplication 33%
• Space Complexity 33%
• Suitable Algorithm 33%
• Grubbs Test 33%
• Hardware Device 33%
• Computational Resource 33%
• Energy Consumption 33%
• Machine Learning 33%
• Data Processing 33%