  Graph Neural Networks (GNNs) have drawn tremendous attention due to their
unique capability to extend Machine Learning (ML) approaches to applications
broadly-defined as having unstructured data, especially graphs. Compared with
other Machine Learning (ML) modalities, the acceleration of Graph Neural
Networks (GNNs) is more challenging due to the irregularity and heterogeneity
derived from graph typologies. Existing efforts, however, have focused mainly
on handling graphs' irregularity and have not studied their heterogeneity.
  To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine)
based hybrid accelerator that leverages the emerging heterogeneity of Xilinx
Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve
high-performance GNN inference. In particular, H-GCN partitions each graph into
three subgraphs based on its inherent heterogeneity, and processes them using
PL and AIE, respectively. To further improve performance, we explore the
sparsity support of AIE and develop an efficient density-aware method to
automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the
systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN
achieves, on average, speedups of 1.1~2.3X.
