ECORE动态调度边缘设备，平衡能耗与检测精度。
ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
- 根据目标特征动态选择最优设备-模型组合
- 能耗降低35%，延迟减少49%，精度仅降2%
- 适合资源受限的实时视觉分析场景
边缘计算将数据处理靠近数据源，显著降低延迟，满足监控和智慧城市中实时视觉分析（如目标检测）的需求。然而，这些任务对资源受限的边缘设备提出高要求，因此需协同优化能耗与检测精度。本文提出ECORE框架，融合多种动态路由策略，包括新型基于估计算法和创新贪心选择算法，将图像处理请求导向最合适的边缘设备-模型组合。该框架根据目标特性动态平衡能效与检测性能。在真实数据集上，通过对比广泛使用的基线方法进行评估，涵盖YOLO、SSD、EfficientDet等主流目标检测模型及Jetson Orin Nano、Raspberry Pi 4/5、TPU加速器等多种边缘平台。结果表明，所提出的上下文感知路由策略可使能耗降低35%，延迟减少49%，同时相比以精度为中心的方法仅损失2%的检测精度。
原文摘要 · Abstract (English)
Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.
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