MicroNAS: Zero-Shot Neural Architecture Search for MCUs

Neural architecture search (NAS) effectively discovers new convolutional neural network (CNN) architectures, particularly for accuracy optimization. However, prior approaches often require resource-intensive training on super networks or extensive architecture evaluations, limiting practical applications. To address these challenges, we propose MicroNAS, a hardware-aware zero-shot NAS framework designed for microcontroller units (MCVs) in edge computing. MicroNAS considers target hardware optimality during the search, utilizing specialized performance indicators to identify optimal neural architectures without heavy computational costs. Compared to previous works, MicroNAS achieves up to
$1104\times$
improvement in search efficiency and discovers models with over
$3.23\times$
faster MCU inference while maintaining similar accuracy.