Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow for Edge-Cloud DL Inference
Model Profiling
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Network Profiling
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Execution Profiling
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Plan Generation
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Plan Actuation
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Inference
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R² Score for Quantization Noise Modeling (Degree 4 Polynomial)
| Feature | Our Approach | Previous Works [10, 11] |
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| Consideration of Quantization Effects |
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YOLO11 Model Evaluation & Performance Gains
The framework was evaluated on complex YOLO11 vision models (652 layers), achieving significant performance gains. Compared to device-only execution without quantization, the approach reduced inference times by up to 33.5% (from 3.64s to 2.42s in device+edge setup) and energy consumption by up to 35.0% (from 14.93J to 9.70J in device+edge setup). Quantization significantly contributed to these improvements, especially in device and device+edge configurations.
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