为超低功耗设备设计微型CNN，可在嵌入式设备上直接搜索模型。
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
- 在嵌入式设备上运行轻量级搜索，直接生成适配硬件的微型CNN
- 在三个小型视觉基准上保持顶尖分类精度，模型极小且低功耗
- 专为传感节点设计，适合资源受限的物联网终端部署
硬件感知神经架构搜索（HW-NAS）通过自动设计符合预设硬件约束的神经网络，使卷积神经网络（CNN）能够部署在微控制器设备上。然而，现有先进HW-NAS主要面向高性能微控制器，其功耗无法满足传感节点的需求。本文提出一种新型HW-NAS，可生成适用于超低功耗微控制器的微型CNN，采用轻量级搜索流程，甚至可在嵌入式设备上直接执行。在三个知名的小型计算机视觉基准上的实验证明，该方法生成的微型CNN在保持顶尖分类准确率的同时，具备极小的模型规模和超低功耗特性。
原文摘要 · Abstract (English)
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.
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