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 energy limitations of smartphones, it is promising to consider deploying pre-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, given a limited connection capacity, a key issue under such a scheme lies in how each 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 accuracy and the latency of individual models 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 edge-assisted mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sublinear regret bound and energy efficiency with high accuracy and low latency.