EExNAS: Early-Exit Neural Architecture Search Solutions for Low-Power Wearable Devices

摘要

Equipping wearable devices with intelligence is essential for promoting mobile healthcare applications. However, challenges remain due to the resource limitations of these devices. In this work, we introduce EExNAS, a methodology for designing high-performance and resource-efficient dynamic Neural Architecture solutions for wearable devices. The methodology incorporates a platform-aware Neural Architecture Search (NAS) that accounts for energy efficiency at runtime through an Early-Exit (EEx) option. We showcase our methodology’s merit across 2 wearable applications, Myocardial Infarction (MI) detection and Human Activity Recognition (HAR). Solutions from EExNAS are compared against those from related works in terms of accuracy and performance. For MI detection, our final solutions with EEx capability could reach 98.54% accuracy on the PTB ECG dataset.