  Graph neural networks (GNN) analysis engines are vital for real-world
problems that use large graph models. Challenges for a GNN hardware platform
include the ability to (a) host a variety of GNNs, (b) handle high sparsity in
input vertex feature vectors and the graph adjacency matrix and the
accompanying random memory access patterns, and (c) maintain load-balanced
computation in the face of uneven workloads, induced by high sparsity and
power-law vertex degree distributions. This paper proposes GNNIE, an
accelerator designed to run a broad range of GNNs. It tackles workload
imbalance by (i)~splitting vertex feature operands into blocks, (ii)~reordering
and redistributing computations, (iii)~using a novel flexible MAC architecture.
It adopts a graph-specific, degree-aware caching policy that is well suited to
real-world graph characteristics. The policy enhances on-chip data reuse and
avoids random memory access to DRAM.
  GNNIE achieves average speedups of 21233x over a CPU and 699x over a GPU over
multiple datasets on graph attention networks (GATs), graph convolutional
networks (GCNs), GraphSAGE, GINConv, and DiffPool. Compared to prior
approaches, GNNIE achieves an average speedup of 35x over HyGCN (which cannot
implement GATs) for GCN, GraphSAGE, and GINConv, and, using 3.4x fewer
processing units, an average speedup of 2.1x over AWB-GCN (which runs only
GCNs).
