在512MB内存设备上运行硬件感知的神经网络搜索，实现物联网端侧小模型定制。
Running hardware-aware neural architecture search on embedded devices under 512MB of RAM
- 基于设备资源动态搜索适合嵌入式部署的微型卷积网络
- 在Visual Wake Word数据集上达成顶尖性能，适配多款低功耗设备
- 无需云端支持即可本地化训练，保障隐私且适用于可穿戴机器人
本文提出一种新型硬件感知神经架构搜索（HW NAS）方法，考虑运行平台的实际资源限制，可在多种嵌入式设备上执行。该方法针对低功耗微控制器单元（MCU）设计极小的卷积神经网络（CNN），适用于物联网（IoT）或可穿戴机器人等场景。网关设备可本地运行此搜索，基于采集数据定制模型架构，无需依赖外部服务器，确保数据隐私。该技术在Visual Wake Word数据集（典型的TinyML基准）上，于多个嵌入式设备上均实现了当前最优性能。
原文摘要 · Abstract (English)
This document proposes a novel approach to hardware-aware neural architecture search (HW NAS) that considers the resources available on the computing platform running it, enabling its execution on various embedded devices. The presented HW NAS produces tiny convolutional neural networks (CNNs) targeting low-end microcontroller units (MCUs), typically involved in the Internet of Things (IoT) or wearable robotics, opening new use cases. A gateway could run it to tailor CNNs' architecture on the acquired data without using external servers, ensuring privacy. The proposed technique achieves state-of-the-art results in the human-recognition tasks on the Visual Wake Word dataset, a standard TinyML benchmark, on several embedded devices.
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