MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via
  Automating Deep Neural Network Porting for Mobile Deployment
Hongtao Huang Xiaojun Chang Wen Hu Lina Yao
Recent years have seen the explosion of edge intelligence with powerful Deep
Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud
servers and subsequently porting them to mobile devices after being
lightweight. Conventional approaches manually specialized DNNs for various edge
platforms and retrain them with real-world data. However, as the number of
platforms increases, these approaches become labour-intensive and
computationally prohibitive. Additionally, real-world data tends to be
sparse-label, further increasing the difficulty of lightweight models. In this
paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices.
Specifically, we simultaneously optimise a large network family using both
labelled and unlabelled data and then automatically search for tailored
networks for different hardware platforms. MatchNAS acts as an intermediary
that bridges the gap between cloud-based DNNs and edge-based DNNs.