Abstract
Deep learning has become a powerful technology in image recognition, gaming, information retrieval, and many other areas that need intelligent data processing. However, huge amount of data and complex computations prevent deep learning from being practical in mobile applications. In this thesis, we proposed a mobile cloud computing system for deep learning. The architecture puts the training process and model repository in cloud platforms, and the recognition process and data gathering in mobile devices. The communication is carried out via Git protocol to ensure the success of data transmission in unstable network environments. We used car camera object detection as an example application, and implemented the system on NVIDIA Jetson TK1. Experiment results show that detection rate can achieve 1 to 4 FPS with Faster R-CNN and ZF model, and the system can work well even when the network connection is unstable.