Performance Analysis of CNN Models for Mobile Device Eye Tracking with Edge Computing.

Eye-tracking is a technique used for determining where users are looking and how long they keep their gaze fixed on a particular location. Developments in mobile technology have made mobile applications pervasive; however, eye tracking on mobile devices is still uncommon. This paper proposes a mobile edge computing architecture for eye tracking. We evaluate four lightweight CNN models (LeNet-5, AlexNet, MobileNet, and ShuffleNet) for gaze estimation on mobile devices using a publicly available dataset called GazeCapture. In order to analyse the feasibility of different inference modes such as on-device, edge-based and cloud-based, we conduct an empirical measurement study to quantify inference time, communication time, and resource consumption in these inference modes. Our analysis indicates that while cloud-based inference provides faster predictions, the communication time between the mobile device and the cloud introduces significant latency into the application. This effectively eliminates the ability to perform real-time eye tracking via cloud inference. Furthermore, our findings show that on-device inference performance is limited by energy and memory consumption, making it unsuitable to provide a high-quality user experience. Additionally, we demonstrated that edge-based inference results in a reasonable response time, memory usage, and energy consumption for eye-tracking applications on mobile devices. [ABSTRACT FROM AUTHOR]