  Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great
power in handling heterogeneous graph data, rendering them widely applied in
many critical real-world domains. Most HGNN models leverage attention
mechanisms to significantly improvemodel accuracy, albeit at the cost of
increased computational complexity and memory bandwidth requirements.
Fortunately, the attention disparity from source vertices towards a common
target vertex unveils an opportunity to boost the model execution performance
by pruning unimportant source vertices during neighbor aggregation. In this
study, we commence with a quantitative analysis of the attention disparity in
HGNN models, where the importance of different source vertices varies for the
same target vertex. To fully exploit this finding for inference acceleration,
we propose a runtime pruning method based on min-heap and map it to a dedicated
hardware pruner to discard unimportant vertices. Given that the pruning
overhead itself is non-negligible and cannot be amortized by conventional
staged execution paradigm, an operation-fusion execution fow of HGNNs is
introduced to overlap the pruning overhead while harnessing inter-stage
parallelism. Finally, we present the design of a novel HGNN accelerator,
ADE-HGNN, tailored to support the proposed execution framework. Our
experimental results demonstrate that ADE-HGNN achieves an average performance
improvement of 28.21x over the NVIDIA GPU T4 platform and 7.98x over the
advanced GPU A100, with the inference accuracy loss kept within a negligible
range of 0.11%~1.47%. Furthermore, ADE-HGNN significantly reduces energy
consumption to 1.97% and 5.37% of the two platforms, respectively.
