  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://github.com/sharc-lab/gnn-builder.
