  Mobile devices run deep learning models for various purposes, such as image
classification and speech recognition. Due to the resource constraints of
mobile devices, researchers have focused on either making a lightweight deep
neural network (DNN) model using model pruning or generating an efficient code
using compiler optimization. Surprisingly, we found that the straightforward
integration between model compression and compiler auto-tuning often does not
produce the most efficient model for a target device. We propose CPrune, a
compiler-informed model pruning for efficient target-aware DNN execution to
support an application with a required target accuracy. CPrune makes a
lightweight DNN model through informed pruning based on the structural
information of subgraphs built during the compiler tuning process. Our
experimental results show that CPrune increases the DNN execution speed up to
2.73x compared to the state-of-the-art TVM auto-tune while satisfying the
accuracy requirement.
