  Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability
of graph representation learning to heterogeneous graphs. However, the
irregular memory access pattern of HGNNs leads to the buffer thrashing issue in
HGNN accelerators. In this work, we identify an opportunity to address buffer
thrashing in HGNN acceleration through an analysis of the topology of
heterogeneous graphs. To harvest this opportunity, we propose a graph
restructuring method and map it into a hardware frontend named GDR-HGNN.
GDR-HGNN dynamically restructures the graph on the fly to enhance data locality
for HGNN accelerators. Experimental results demonstrate that, with the
assistance of GDR-HGNN, a leading HGNN accelerator achieves an average speedup
of 14.6 times and 1.78 times compared to the state-of-the-art software
framework running on A100 GPU and itself, respectively.
