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 work 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.

翻译：硬件感知神经架构搜索（HW-NAS）允许通过在微控制器设备上集成卷积神经网络（CNN），自动设计能够满足预设硬件约束的神经架构。然而，当前最先进的HW-NAS针对的是高性能微控制器，其功耗无法满足传感节点的要求。本研究提出了一种HW-NAS，用于生成可在超低功耗微控制器上运行的微型CNN，该方案采用轻量级搜索流程，甚至能够在嵌入式设备上执行。在三个著名微型计算机视觉基准测试上的实验结果表明，所提出的HW-NAS能够在保持当前最优分类精度的同时生成微型CNN。