  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
(MCUs) in edge computing. MicroNAS considers target hardware optimality during
the search, utilizing specialized performance indicators to identify optimal
neural architectures without high computational costs. Compared to previous
works, MicroNAS achieves up to 1104x improvement in search efficiency and
discovers models with over 3.23x faster MCU inference while maintaining similar
accuracy
