Designing low-latency and high-efficiency hybrid networks for a variety of low-cost commodity edge devices is both costly and tedious, leading to the adoption of hardware-aware neural architecture search (NAS) for finding optimal architectures. However, unifying NAS for a wide range of edge devices presents challenges due to the variety of hardware designs, supported operations, and compilation optimizations. Existing methods often fix the search space of architecture choices (e.g., activation, convolution, or self-attention) and estimate latency using hardware-agnostic proxies (e.g., FLOPs), which fail to achieve proclaimed latency across various edge devices. To address this issue, we propose SCAN-Edge, a unified NAS framework that jointly searches for self-attention, convolution, and activation to accommodate the wide variety of edge devices, including CPU-, GPU-, and hardware accelerator-based systems. To handle the large search space, SCAN-Edge relies on with a hardware-aware evolutionary algorithm that improves the quality of the search space to accelerate the sampling process. Experiments on large-scale datasets demonstrate that our hybrid networks match the actual MobileNetV2 latency for 224x224 input resolution on various commodity edge devices.

本研究解决了为多种低成本边缘设备设计低延迟和高效率混合网络所面临的复杂性和成本问题。提出的SCAN-Edge是一种统一的神经架构搜索框架，通过硬件感知进化算法共同搜索自注意力、卷积和激活，以应对不同边缘设备的需求。实验表明，所提出的混合网络在各种商品边缘设备上与实际MobileNetV2的延迟表现一致，具有显著的实际应用潜力。

SCAN-Edge：通过硬件感知进化搜索为多样化边缘设备寻找MobileNet速度混合网络