  High-end mobile platforms rapidly serve as primary computing devices for a
wide range of Deep Neural Network (DNN) applications. However, the constrained
computation and storage resources on these devices still pose significant
challenges for real-time DNN inference executions. To address this problem, we
propose a set of hardware-friendly structured model pruning and compiler
optimization techniques to accelerate DNN executions on mobile devices. This
demo shows that these optimizations can enable real-time mobile execution of
multiple DNN applications, including style transfer, DNN coloring and super
resolution.
