Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typical GNN models indicates that their execution characteristics are significantly affected across different computing platforms, which demands hardware awareness for efficient GNN designs. In this work, HGNAS is proposed as the first Hardware-aware Graph Neural Architecture Search framework targeting resource constraint edge devices. By decoupling the GNN paradigm, HGNAS constructs a fine-grained design space and leverages an efficient multi-stage search strategy to explore optimal architectures within a few GPU hours. Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices. Experimental results show that HGNAS can achieve about $10.6\times$ speedup and $88.2\%$ peak memory reduction with a negligible accuracy loss compared to DGCNN on various edge devices, including Nvidia RTX3080, Jetson TX2, Intel i7-8700K and Raspberry Pi 3B+.

翻译：图神经网络（GNN）因在非欧几里得数据上表现出的先进性能而成为一种流行策略。然而，当前大多数GNN模型设计主要关注任务精度，缺乏对边缘应用场景中硬件资源限制和实时性需求的考虑。对典型GNN模型的全面分析表明，其执行特性在不同计算平台上受到显著影响，这要求在高效的GNN设计中具备硬件感知能力。本文提出HGNAS，这是首个面向资源受限边缘设备的硬件感知图神经架构搜索框架。通过解耦GNN范式，HGNAS构建了细粒度的设计空间，并利用高效的多阶段搜索策略在数GPU小时内探索最优架构。此外，HGNAS通过引入硬件性能预测器，在GNN架构设计过程中实现硬件感知，从而根据目标设备的特性平衡GNN模型精度与效率。实验结果表明，在包括Nvidia RTX3080、Jetson TX2、Intel i7-8700K和Raspberry Pi 3B+在内的多种边缘设备上，与DGCNN相比，HGNAS可实现约10.6倍的加速比和88.2%的峰值内存减少，且精度损失可忽略不计。