HGNAS: <underline>H</underline>ardware-Aware <underline>G</underline>raph <underline>N</underline>eural <underline>A</underline>rchitecture <underline>S</underline>earch for Edge Devices

Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking consideration of how to design efficient GNN models for edge scenarios with real-time requirements and limited resources. Examining existing GNN models reveals varied execution across platforms and frequent Out-Of-Memory (OOM) problems, highlighting the need for hardware-aware GNN design. To address this challenge, this work proposes a novel hardware-aware graph neural architecture search framework tailored for resource constraint edge devices, namely HGNAS. To achieve hardware awareness, HGNAS integrates an efficient GNN hardware performance predictor that evaluates the latency and peak memory usage of GNNs in milliseconds. Meanwhile, we study GNN memory usage during inference and offer a peak memory estimation method, enhancing the robustness of architecture evaluations when combined with predictor outcomes. Furthermore, HGNAS constructs a fine-grained design space to enable the exploration of extreme performance architectures by decoupling the GNN paradigm. In addition, the multi-stage hierarchical search strategy is leveraged to facilitate the navigation of huge candidates, which can reduce the single search time to a few GPU hours. To the best of our knowledge, HGNAS is the first automated GNN design framework for edge devices, and also the first work to achieve hardware awareness of GNNs across different platforms. Extensive experiments across various applications and edge devices have proven the superiority of HGNAS. It can achieve up to a <inline-formula><tex-math notation="LaTeX">$10.6\boldsymbol{\times}$</tex-math><alternatives><mml:math display="inline"><mml:mn>10.6</mml:mn><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="yang-ieq1-3449108.gif"/></alternatives></inline-formula> speedup and an <inline-formula><tex-math notation="LaTeX">$82.5\%$</tex-math><alternatives><mml:math display="inline"><mml:mn>82.5</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq2-3449108.gif"/></alternatives></inline-formula> peak memory reduction with negligible accuracy loss compared to DGCNN on ModelNet40.