  Transformer-based deep learning models have increasingly demonstrated high
accuracy on many natural language processing (NLP) tasks. In this paper, we
propose a compression-compilation co-design framework that can guarantee the
identified model to meet both resource and real-time specifications of mobile
devices. Our framework applies a compiler-aware neural architecture
optimization method (CANAO), which can generate the optimal compressed model
that balances both accuracy and latency. We are able to achieve up to 7.8x
speedup compared with TensorFlow-Lite with only minor accuracy loss. We present
two types of BERT applications on mobile devices: Question Answering (QA) and
Text Generation. Both can be executed in real-time with latency as low as 45ms.
Videos for demonstrating the framework can be found on
https://www.youtube.com/watch?v=_WIRvK_2PZI
