Green Edge Intelligence Scheme for Mobile Keyboard Emoji Prediction

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the resource constraints of smartphones, it is promising to deploy well-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, a key issue in such a scenario lies in how the smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency (a.k.a. the model selection problem). Moreover, part of the system dynamics such as the prediction accuracy and the inference latency of each model are usually unknown a priori in practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sub-linear round-averaged regret bound and energy efficiency with a high prediction accuracy and a low latency.