  Along with the rapid development in the field of artificial intelligence,
especially deep learning, deep neural network applications are becoming more
and more popular in reality. To be able to withstand the heavy load from
mainstream users, deployment techniques are essential in bringing neural
network models from research to production. Among the two popular computing
topologies for deploying neural network models in production are
cloud-computing and edge-computing. Recent advances in communication
technologies, along with the great increase in the number of mobile devices,
has made edge-computing gradually become an inevitable trend. In this paper, we
propose an architecture to solve deploying and processing deep neural networks
on edge-devices by leveraging their synergy with the cloud and the
access-control mechanisms of the database. Adopting this architecture allows
low-latency DNN model updates on devices. At the same time, with only one model
deployed, we can easily make different versions of it by setting access
permissions on the model weights. This method allows for dynamic model
licensing, which benefits commercial applications.
