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.

翻译：本文提出了一种新颖的硬件感知神经架构搜索（HW NAS）方法，该方法充分考虑了运行计算平台可用资源，使其能够在各类嵌入式设备上执行。所提出的HW NAS技术可为低端微控制器单元（MCU）生成微型卷积神经网络（CNN），这些MCU通常应用于物联网（IoT）或可穿戴机器人领域，从而开辟了新的应用场景。网关设备可无需借助外部服务器，直接在采集数据上运行该技术以定制CNN架构，确保数据隐私。在多个嵌入式设备上的Visual Wake Word数据集（一项标准TinyML基准测试）中，所提技术在人体识别任务上达到了当前最优水平。