EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devices
This addresses energy efficiency for video analytics on resource-constrained edge devices, but it is incremental as it builds on existing optimization techniques.
The paper tackles the problem of energy-efficient video processing on edge devices by proposing a system that dynamically optimizes configurations to minimize energy usage while preserving video features for deep learning inference, achieving reduced energy consumption with maintained high analytical performance.
Video processing for real-time analytics in resource-constrained environments presents a significant challenge in balancing energy consumption and video semantics. This paper addresses the problem of energy-efficient video processing by proposing a system that dynamically optimizes processing configurations to minimize energy usage on the edge, while preserving essential video features for deep learning inference. We first gather an extensive offline profile of various configurations consisting of device CPU frequencies, frame filtering features, difference thresholds, and video bitrates, to establish apriori knowledge of their impact on energy consumption and inference accuracy. Leveraging this insight, we introduce an online system that employs multi-objective Bayesian optimization to intelligently explore and adapt configurations in real time. Our approach continuously refines processing settings to meet a target inference accuracy with minimal edge device energy expenditure. Experimental results demonstrate the system's effectiveness in reducing video processing energy use while maintaining high analytical performance, offering a practical solution for smart devices and edge computing applications.