ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
This work addresses energy efficiency and latency issues for real-time vision-based analytics in edge computing environments like surveillance and smart cities, representing an incremental improvement with specific gains.
The paper tackles the challenge of optimizing energy consumption and detection accuracy for object detection on resource-constrained edge devices by proposing ECORE, a framework that uses dynamic routing strategies to direct image processing requests, resulting in a 35% reduction in energy consumption, 49% reduction in latency, and only a 2% loss in accuracy compared to accuracy-centric methods.
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.