  Following the recent success of deep neural networks (DNN) on video computer
vision tasks, performing DNN inferences on videos that originate from mobile
devices has gained practical significance. As such, previous approaches
developed methods to offload DNN inference computations for images to cloud
servers to manage the resource constraints of mobile devices. However, when it
comes to video data, communicating information of every frame consumes
excessive network bandwidth and renders the entire system susceptible to
adverse network conditions such as congestion. Thus, in this work, we seek to
exploit the temporal coherence between nearby frames of a video stream to
mitigate network pressure. That is, we propose ShadowTutor, a distributed video
DNN inference framework that reduces the number of network transmissions
through intermittent knowledge distillation to a student model. Moreover, we
update only a subset of the student's parameters, which we call partial
distillation, to reduce the data size of each network transmission.
Specifically, the server runs a large and general teacher model, and the mobile
device only runs an extremely small but specialized student model. On sparsely
selected key frames, the server partially trains the student model by targeting
the teacher's response and sends the updated part to the mobile device. We
investigate the effectiveness of ShadowTutor with HD video semantic
segmentation. Evaluations show that network data transfer is reduced by 95% on
average. Moreover, the throughput of the system is improved by over three times
and shows robustness to changes in network bandwidth.
