Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy optimization goals. However, previous approaches often involve time-consuming training on super networks or intensive architecture sampling and evaluations. Although various zero-cost proxies correlated with CNN model accuracy have been proposed for efficient architecture search without training, their lack of hardware consideration makes it challenging to target highly resource-constrained edge devices such as microcontroller units (MCUs). To address these challenges, we introduce MONAS, a novel hardware-aware zero-shot NAS framework specifically designed for MCUs in edge computing. MONAS incorporates hardware optimality considerations into the search process through our proposed MCU hardware latency estimation model. By combining this with specialized performance indicators (proxies), MONAS identifies optimal neural architectures without incurring heavy training and evaluation costs, optimizing for both hardware latency and accuracy under resource constraints. MONAS achieves up to a 1104x improvement in search efficiency over previous work targeting MCUs and can discover CNN models with over 3.23x faster inference on MCUs while maintaining similar accuracy compared to more general NAS approaches.

本文解决了在资源受限的边缘设备（如微控制器）上进行高效神经架构搜索的问题，以前的方法未考虑硬件限制。提出的MONAS框架结合了硬件延迟估计模型和专门的性能指标，无需大量训练和评估即可识别最佳神经架构。研究表明，MONAS在搜索效率上比以往工作提高了1104倍，并且在保持相似精度的同时，微控制器上的推理速度提升了3.23倍。

MONAS：面向微控制器的高效零-shot神经架构搜索