  Recent studies have shown that Binary Graph Neural Networks (GNNs) are
promising for saving computations of GNNs through binarized tensors. Prior
work, however, mainly focused on algorithm designs or training techniques,
leaving it open to how to materialize the performance potential on accelerator
hardware fully. This work redesigns the binary GNN inference backend from the
efficiency perspective. It fills the gap by proposing a series of abstractions
and techniques to map binary GNNs and their computations best to fit the nature
of bit manipulations on GPUs. Results on real-world graphs with GCNs,
GraphSAGE, and GraphSAINT show that the proposed techniques outperform
state-of-the-art binary GNN implementations by 8-22X with the same accuracy
maintained. BitGNN code is publicly available.
