  Graph Neural Networks (GNNs) use a fully-connected layer to extract features
from the nodes of a graph and aggregate these features using message passing
between nodes, combining two distinct computational patterns: dense, regular
computations and sparse, irregular computations.
  To address this challenge, we propose GNNerator, an accelerator with
heterogeneous compute engines optimized for these two patterns.
  Further, GNNerator implements feature-blocking, a novel GNN dataflow that
beneficially trades off irregular memory accesses during aggregation for
regular memory accesses during feature extraction. We show GNNerator achieves
speedups of 5.7-37x over an NVIDIA RTX 2080-Ti, and 2.3x-3.8x over HyGCN, a
state-of-the-art GNN accelerator.
