With the glow of digital information techniques, mobile systems are powerful ever and occupying more market shares. Just like wildly used social media sites, e.g. Facebook and Twitter, smartphone usage is up to 80% by 2020. In parallel to this trend, many companies are trying to incorporate Artificial Intelligent especially deep learning empowered applications into devices to further ease the life of people. At an early stage, the models are offloaded to the cloud and require frequent data transfer to perform inference since in the mobile end the high computational consumption and complex structure of deep learning models are not affordable. In recent years they are trying to lighten the deep learning models to enable inference and even training at mobile devices directly. Techniques like pruning, quantizing, knowledge distillation are developed to delimitate computation labour of computer vision (CV) tasks. In addition to the help of a new mobile end, deep learning framework (Tensorflow lite, PytorchMobile, CoreML) makes the market of on-device applications getting bigger and bigger due to the low latency and better privacy protection for individuals.