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Mobile video applications today have attracted significant attention. Deeplearning model (e.g. deep neural network, DNN) compression is widely used toenable on-device inference for facilitating robust and private mobile videoapplications. The compressed DNN, however, is vulnerable to the agnostic datadrift of the live video captured from the dynamically changing mobilescenarios. To combat the data drift, mobile ends rely on edge servers tocontinuously evolve and re-compress the DNN with freshly collected data. Wedesign a framework, AdaEvo, that efficiently supports the resource-limited edgeserver handling mobile DNN evolution tasks from multiple mobile ends. The keygoal of AdaEvo is to maximize the average quality of experience (QoE), e.g. theproportion of high-quality DNN service time to the entire life cycle, for allmobile ends. Specifically, it estimates the DNN accuracy drops at the mobileend without labels and performs a dedicated video frame sampling strategy tocontrol the size of retraining data. In addition, it balances the limitedcomputing and memory resources on the edge server and the competition betweenasynchronous tasks initiated by different mobile users. With an extensiveevaluation of real-world videos from mobile scenarios and across four diversemobile tasks, experimental results show that AdaEvo enables up to 34% accuracyimprovement and 32% average QoE improvement.

AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices