An Affordable Hardware-Aware Neural Architecture Search for Deploying Convolutional Neural Networks on Ultra-Low-Power Computing Platforms

• Published in: IEEE sensors letters 2024-05, Vol.8 (5), p.1-4
• Main Authors: Garavagno, Andrea Mattia, Ragusa, Edoardo, Frisoli, Antonio, Gastaldo, Paolo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Computer architecture; Computer vision; Costs; Embedded systems; Hardware; hardware-aware neural architecture search (HW-NAS); Kernel; Microcontrollers; Power consumption; Power management; Random access memory; Search problems; Searching; Sensor applications; Sensors; TinyML; Training; ultra-low-power computing
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2024.3387056

Hardware-aware neural architecture search (HW-NAS) allows the integration of convolutional neural networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints. However, state-of-the-art HW-NAS target high-performance microcontrollers, whose power consumption does not meet sensing nodes requirements. This letter presents a HW-NAS generating tiny CNNs that can run on ultra-low-power microcontrollers, featuring a lightweight search procedure enabling its execution even on embedded devices. Empirical results on three well-known benchmarks for tiny computer vision proved that the proposed HW-NAS was able to generate tiny CNNs while preserving state-of-the-art classification accuracy.