TouchNAS: Efficient Touch Detection Model Design Methodology for Resource-Constrained Devices

Abstract

The multi-touch algorithm in touch screens is one of the most important interaction tasks for functional operations. This algorithm classifies multiple objects and recognizes the touch positions. However, rule-based algorithms have limited detection performance for small and closely-spaced touch objects. Deep learning-based multi-touch algorithms achieve higher accuracy than conventional rule-based methods. However, the implementation of state-of-the-art multi-touch algorithms in hardware faces the critical problem that the latency and necessary hardware resources are greatly increased owing to the significant computational complexity. Neural architecture search (NAS) has recently been recognized as an effective solution to search for optimal deep learning models in hardware. This paper proposes an efficient NAS algorithm, TouchNAS, to find the optimal multi-touch algorithm on a target device. We first introduce the progressive architecture pruning method for the search space. This method reduces the training and evaluation costs by omitting the accuracy predictor. We also propose a dynamic learning rate scheduler that assists the efficient training of multiple models by calculating the number of model parameters. Finally, we present multi-touch datasets to measure the performance of multi-touch algorithms objectively. The experimental results on various deep learning models show that our TouchNAS identifies only the candidates for the optimal model within the entire search space for selection and training. Moreover, the optimal model found by TouchNAS achieves superior performance over state-of-the-art NAS methods.