E4通过动态调整退出点和频率，让边缘视频分析更省电更快。
E4: Energy-Efficient DNN Inference for Edge Video Analytics Via Early-Exit and DVFS
- 用注意力机制分析视频帧差异，自动选最优退出点
- 每层前用坐标下降法优化CPU/GPU频率，最多省26%电
- 适合资源受限的边缘设备，兼顾速度与能效
深度神经网络（DNN）在边缘视频分析中应用日益广泛，但其计算密集特性给资源受限的边缘设备带来了能源效率挑战。现有方案多关注推理延迟与精度，常忽略能效问题，且未考虑视频帧间复杂度差异，导致边缘视频分析性能不佳。本文提出一种节能早退框架E4，通过融合新型早退机制与动态电压频率调节（DVFS）调度器，提升边缘视频分析中的DNN推理效率。E4采用基于注意力的级联模块，分析视频帧多样性并自动确定最优的DNN退出点；同时引入即时（JIT）剖析器，使用坐标下降搜索法，在每个退出点前协同优化CPU与GPU时钟频率。大量实验表明，E4优于当前最先进方法，实现最高2.8倍加速，平均节省26%能耗，同时保持高精度。
原文摘要 · Abstract (English)
Deep neural network (DNN) models are increasingly popular in edge video analytic applications. However, the compute-intensive nature of DNN models pose challenges for energy-efficient inference on resource-constrained edge devices. Most existing solutions focus on optimizing DNN inference latency and accuracy, often overlooking energy efficiency. They also fail to account for the varying complexity of video frames, leading to sub-optimal performance in edge video analytics. In this paper, we propose an Energy-Efficient Early-Exit (E4) framework that enhances DNN inference efficiency for edge video analytics by integrating a novel early-exit mechanism with dynamic voltage and frequency scaling (DVFS) governors. It employs an attention-based cascade module to analyze video frame diversity and automatically determine optimal DNN exit points. Additionally, E4 features a just-in-time (JIT) profiler that uses coordinate descent search to co-optimize CPU and GPU clock frequencies for each layer before the DNN exit points. Extensive evaluations demonstrate that E4 outperforms current state-of-the-art methods, achieving up to 2.8x speedup and 26% average energy saving while maintaining high accuracy.
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