Neural Architecture Search for Light-weight Multi-touch Classification

Multi-touch algorithm has proven its effectiveness in various touch applications. Recently, using convolutional neural network were shown to be effective in accurately classifying multi-touch inputs. However, multi-touch algorithm requires very low computational complexity and size due to the resource limitations of target hardwares. Neural Architecture Search (NAS) is currently being spotlighted as an effective solution to designing optimal light-weight networks. Especially, Once-for-all NAS shows remarkable performance in searching for optimal networks on various hardware platforms. In this paper, we propose an efficient OFA NAS based method for designing optimal CNN based multi-touch classifier with a new shrunk search space. The model searched by our proposed method shows outstanding performance despite its computational simplicity. Compared to MobileNetV2, our model has 7 times less MACs with only 1.25% accuracy drop.