Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.

翻译：当前移动视频应用备受关注。深度学习模型（如深度神经网络DNN）的压缩技术被广泛用于实现设备端推理，以支持稳健且私密的移动视频应用。然而，压缩后的DNN容易受到动态变化的移动场景中实时视频所导致的未知数据漂移的影响。为应对数据漂移，移动终端依赖边缘服务器利用新采集的数据持续演化并重新压缩DNN。我们设计了自适应演化框架AdaEvo，可高效支持资源受限的边缘服务器处理来自多个移动终端的DNN演化任务。AdaEvo的核心目标是最大化所有移动终端的平均服务质量体验（QoE），例如高质量DNN服务时间占整个生命周期的比例。具体而言，该框架无需标签即可估计移动终端侧DNN精度下降，并采用专用的视频帧采样策略控制重训练数据量。此外，它还能平衡边缘服务器有限的计算与内存资源，以及不同移动用户发起的异步任务之间的竞争。基于真实移动场景视频及四项不同移动任务的广泛评估表明，AdaEvo能实现高达34%的精度提升和32%的平均QoE提升。