There are plenty of graph neural network (GNN) accelerators being proposed.
However, they highly rely on users' hardware expertise and are usually
optimized for one specific GNN model, making them challenging for practical use
. Therefore, in this work, we propose GNNBuilder, the first automated, generic,
end-to-end GNN accelerator generation framework. It features four advantages:
(1) GNNBuilder can automatically generate GNN accelerators for a wide range of
GNN models arbitrarily defined by users; (2) GNNBuilder takes standard PyTorch
programming interface, introducing zero overhead for algorithm developers; (3)
GNNBuilder supports end-to-end code generation, simulation, accelerator
optimization, and hardware deployment, realizing a push-button fashion for GNN
accelerator design; (4) GNNBuilder is equipped with accurate performance models
of its generated accelerator, enabling fast and flexible design space
exploration (DSE). In the experiments, first, we show that our accelerator
performance model has errors within $36\%$ for latency prediction and $18\%$
for BRAM count prediction. Second, we show that our generated accelerators can
outperform CPU by $6.33\times$ and GPU by $6.87\times$. This framework is
open-source, and the code is available at
https://anonymous.4open.science/r/gnn-builder-83B4/.

本文介绍了 GNNBuilder，这是第一个自动化的、通用的、端到端的 GNN 加速器生成框架，支持各种由用户任意定义的 GNN 模型，并拥有精确的性能模型和设计空间探索功能。实验结果显示，GNNBuilder 生成的加速器可以比 CPU 快 6.33 倍，比 GPU 快 6.87 倍。