用智能优化降低边缘设备视频处理能耗，同时保持分析精度。
EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devices
- 通过贝叶斯优化动态调整帧过滤、码率等参数
- 实测能耗降低40%以上，推理准确率损失低于5%
- 适合物联网与实时视频分析场景的低功耗部署
在资源受限的边缘环境中，实时视频分析需平衡能耗与视频语义保留。本文提出EcoLens系统，通过离线采集设备CPU频率、帧过滤特征、差异阈值和视频码率等多种配置组合的能耗与精度数据，建立先验知识。在线阶段采用多目标贝叶斯优化，实时探索并自适应调整配置，在满足目标推理精度的前提下最小化边缘设备能耗。实验表明，该方法可显著降低视频处理能耗，同时保持高分析性能，为智能设备与边缘计算应用提供实用的节能方案。
原文摘要 · Abstract (English)
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.
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