G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
This work addresses the problem of scaling GNNs for large-scale real-world applications by improving both accuracy and efficiency, representing a novel integration rather than an incremental step.
The paper tackles the challenge of inefficient GNN inference on large graphs by proposing G-CoS, a co-search framework that automatically finds matched GNN structures and accelerators, resulting in GNNs and accelerators that outperform state-of-the-art methods in both accuracy and hardware efficiency with generation times of only a few hours.
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.