EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devices

TL;DR

EcoLens presents a multi-objective Bayesian optimization system that dynamically adjusts video processing configurations on edge devices to reduce energy consumption while maintaining inference accuracy, enabling efficient real-time analytics.

Contribution

This work introduces a novel online optimization framework that combines offline profiling with Bayesian methods for energy-efficient video processing on resource-constrained edge devices.

Findings

Significant reduction in energy consumption during video processing.

Maintains high inference accuracy with adaptive configuration tuning.

Demonstrates practical applicability in edge computing scenarios.

Abstract

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.