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

翻译：神经架构搜索（NAS）能够有效发现新的卷积神经网络（CNN）架构，尤其适用于精度优化。然而，现有方法通常需要在超网络上进行资源密集型训练或进行大量架构评估，限制了实际应用。为解决这些挑战，我们提出了MicroNAS——一种面向边缘计算中微控制器单元（MCU）的硬件感知零样本NAS框架。MicroNAS在搜索过程中考虑目标硬件最优性，利用专用性能指标在不产生高计算成本的情况下识别最优神经架构。与先前工作相比，MicroNAS的搜索效率提升了高达1104倍，并能在保持相似精度的同时，发现MCU推理速度提高3.23倍以上的模型。