  Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA)
method for graph-based learning tasks. However, it still remains prohibitively
challenging to inference GNNs over large graph datasets, limiting their
application to large-scale real-world tasks. While end-to-end jointly
optimizing GNNs and their accelerators is promising in boosting GNNs' inference
efficiency and expediting the design process, it is still underexplored due to
the vast and distinct design spaces of GNNs and their accelerators. In this
work, we propose G-CoS, a GNN and accelerator co-search framework that can
automatically search for matched GNN structures and accelerators to maximize
both task accuracy and acceleration efficiency. Specifically, GCoS integrates
two major enabling components: (1) a generic GNN accelerator search space which
is applicable to various GNN structures and (2) a one-shot GNN and accelerator
co-search algorithm that enables simultaneous and efficient search for optimal
GNN structures and their matched accelerators. To the best of our knowledge,
G-CoS is the first co-search framework for GNNs and their accelerators.
Extensive experiments and ablation studies show that the GNNs and accelerators
generated by G-CoS consistently outperform SOTA GNNs and GNN accelerators in
terms of both task accuracy and hardware efficiency, while only requiring a few
hours for the end-to-end generation of the best matched GNNs and their
accelerators.
