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

TL;DR

AdaEvo is a framework that enables resource-efficient, continuous evolution of deep neural networks on edge servers to adapt to data drift in mobile video applications, significantly improving accuracy and user experience.

Contribution

It introduces a novel edge-assisted DNN evolution framework that estimates accuracy drops without labels and balances resource constraints for multiple mobile users.

Findings

Achieves up to 34% accuracy improvement.

Improves average QoE by 32%.

Effectively handles real-world mobile video data.

Abstract

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.