XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
In recent advancements in AI, a novel framework known as XiYOLO has emerged, targeting the challenges of energy-efficient object detection on heterogeneous edge devices. This innovative approach addresses the critical need for reliable perception in autonomous systems while adhering to stringent energy, latency, and memory constraints.
The research, detailed in the paper titled “XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling,” presents an energy-adaptive framework designed to optimize object detection models, particularly in environments where energy resources are limited and device characteristics vary significantly.
Key Features of XiYOLO Framework
- Energy-Aware Search Space: The framework introduces an energy-aware XiResOFA (eXtreme Energy Resilient One-For-All) search space that allows for the identification of a single energy-efficient base architecture tailored for specific deployment scenarios.
- Two-Stage Energy Estimator: A crucial component of the XiYOLO framework is its two-stage energy estimator, which provides a more accurate and efficient estimation of energy consumption compared to previous methods. This estimator enhances the model’s adaptability to real-world conditions by factoring in device-dependent energy metrics.
- Iterative Search Methodology: The iterative search process is designed to refine the architecture progressively, ensuring optimal performance across various energy budgets. This approach allows for the generation of multiple variations of the base model, catering to different operational constraints.
- Compound Scaling: By applying compound scaling techniques to the base architecture, the XiYOLO family can be developed to accommodate diverse deployment budgets, creating a range of models that maintain a balance between accuracy and energy efficiency.
Performance Metrics
Experiments conducted on widely recognized datasets such as PascalVOC and COCO demonstrate the effectiveness of the XiYOLO framework. The results indicate a significant improvement in the energy-accuracy tradeoff when compared to traditional YOLO baselines.
- On the PascalVOC dataset, the medium XiYOLO model achieved a mean Average Precision (mAP50) of 86.15 while reducing energy usage by 20.6% on GPU and 35.9% on NPU when compared to YOLOv12m.
- In trials utilizing the COCO dataset, XiYOLO showed a reduction in energy consumption of up to 53.7% on GPU and 51.6% on NPU at the small scale, demonstrating its superior efficiency.
- The two-stage energy estimator also showcased improved sample efficiency, particularly in few-shot adaptation scenarios, requiring only 2-20 target-device samples for effective training.
Conclusion
The introduction of XiYOLO marks a significant advancement in the field of energy-aware object detection, particularly for edge devices. By combining innovative architectural search techniques, precise energy estimation, and scalable design principles, XiYOLO not only meets the demands of modern autonomous systems but also sets a new benchmark for energy efficiency in AI applications. As the research community continues to explore the implications of this framework, its potential to revolutionize object detection in energy-constrained environments becomes increasingly apparent.
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