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

• URL: http://arxiv.org/abs/2506.00754v1
• Date: Sat, 31 May 2025 23:47:47 GMT
• Title: EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devices
• Authors: Benjamin Civjan, Bo Chen, Ruixiao Zhang, Klara Nahrstedt,
• Abstract summary: Video processing for real-time analytics in resource-constrained environments presents a challenge in balancing energy consumption and video semantics.<n>This paper proposes a system that dynamically optimize processing to minimize energy usage on the edge, while preserving essential video features for deep learning inference.<n> Experimental results demonstrate the system's effectiveness in reducing video processing energy use while maintaining high analytical performance.
• Score: 8.957154727556697
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Energy Considerations of Large Language Model Inference and Efficiency Optimizations [28.55549828393871]
  As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise.<n>We systematically analyze the energy implications of common inference efficiency optimizations across diverse NLP and AI workloads.<n>Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines.
  arXiv  Detail & Related papers  (2025-04-24T15:45:05Z)
• Mondrian: On-Device High-Performance Video Analytics with Compressive Packed Inference [7.624476059109304]
  Mondrian is an edge system that enables high-performance object detection on high-resolution video streams. We devise a novel Compressive Packed Inference to minimize per-pixel processing costs.
  arXiv  Detail & Related papers  (2024-03-12T12:35:12Z)
• PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices [10.01838504586422]
  The continuous operation of ML-powered systems leads to significant energy use during inference. This paper investigates how the configuration of on-device hardware-elements such as GPU, memory, and CPU frequency, affects energy consumption for NN inference with regular fine-tuning. We propose PolyThrottle, a solution that optimize configurations across individual hardware components using Constrained Bayesian Optimization in an energy-conserving manner.
  arXiv  Detail & Related papers  (2023-10-30T20:19:41Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices [90.30316433184414]
  We propose a data-model-hardware tri-design framework for high- throughput, low-cost, and high-accuracy MOT on HD video stream. Compared to the state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x latency reduction, 20.9x effective frame rate improvement, 5.83x lower power, and 9.78x better energy efficiency, without much accuracy drop.
  arXiv  Detail & Related papers  (2022-10-16T16:21:40Z)
• Efficient Heterogeneous Video Segmentation at the Edge [2.4378845585726903]
  We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across multiple dimensions of specifications for the neural architectures. We analyze and optimize the heterogeneous data flows in our systems across the CPU, the GPU and the NPU.
  arXiv  Detail & Related papers  (2022-08-24T17:01:09Z)
• Energy-Efficient Multi-Orchestrator Mobile Edge Learning [54.28419430315478]
  Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices. In MEL, possible coexistence of multiple learning tasks with different datasets may arise. We propose lightweight algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity.
  arXiv  Detail & Related papers  (2021-09-02T07:37:10Z)
• Dynamic Network Quantization for Efficient Video Inference [60.109250720206425]
  We propose a dynamic network quantization framework, that selects optimal precision for each frame conditioned on the input for efficient video recognition. We train both networks effectively using standard backpropagation with a loss to achieve both competitive performance and resource efficiency.
  arXiv  Detail & Related papers  (2021-08-23T20:23:57Z)
• A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline [16.72264118199915]
  Video analytics pipelines are energy-intensive due to high data rates and reliance on complex inference algorithms. We propose an adaptive-resolution optimization framework to minimize the energy use of multi-task video analytics pipelines. Our framework has significantly surpassed all baseline methods of similar accuracy on the YouTube-VIS dataset.
  arXiv  Detail & Related papers  (2021-04-09T15:44:06Z)
• Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization [96.73647162960842]
  TAL is a fundamental yet challenging task in video understanding. Existing TAL methods rely on pre-training a video encoder through action classification supervision. We introduce a novel low-fidelity end-to-end (LoFi) video encoder pre-training method.
  arXiv  Detail & Related papers  (2021-03-28T22:18:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.