With the ever-growing popularity of Graph Neural Networks (GNNs), efficient
GNN inference is gaining tremendous attention. Field-Programming Gate Arrays
(FPGAs) are a promising execution platform due to their fine-grained
parallelism, low-power consumption, reconfigurability, and concurrent
execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between
the non-trivial FPGA development efforts and rapid emergence of new GNN models.
In this paper, we propose GNNHLS, an open-source framework to comprehensively
evaluate GNN inference acceleration on FPGAs via HLS, containing a software
stack for data generation and baseline deployment, and FPGA implementations of
6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with
distinct topologies and scales. The results show that GNNHLS achieves up to
50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared
with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy
reduction.

通过高级综合技术，在 FPGA 上加速图神经网络推理，实现了高达 50.8 倍的加速和 423 倍的能量降低，与 CPU 基线相比，以及高达 5.16 倍的加速和 74.5 倍的能量降低，与 GPU 基线相比。

GNNHLS：通过高层综合评估图神经网络推断

GNNHLS: Evaluating Graph Neural Network Inference via High-Level  Synthesis

With graphs rapidly growing in size and deeper graph neural networks (GNNs)
emerging, the training and inference of GNNs become increasingly expensive.
Existing network weight pruning algorithms cannot address the main space and
computational bottleneck in GNNs, caused by the size and connectivity of the
graph. To this end, this paper first presents a unified GNN sparsification
(UGS) framework that simultaneously prunes the graph adjacency matrix and the
model weights, for effectively accelerating GNN inference on large-scale
graphs. Leveraging this new tool, we further generalize the recently popular
lottery ticket hypothesis to GNNs for the first time, by defining a graph
lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network,
which can be jointly identified from the original GNN and the full dense graph
by iteratively applying UGS. Like its counterpart in convolutional neural
networks, GLT can be trained in isolation to match the performance of training
with the full model and graph, and can be drawn from both randomly initialized
and self-supervised pre-trained GNNs. Our proposal has been experimentally
verified across various GNN architectures and diverse tasks, on both
small-scale graph datasets (Cora, Citeseer and PubMed), and large-scale
datasets from the challenging Open Graph Benchmark (OGB). Specifically, for
node classification, our found GLTs achieve the same accuracies with 20%~98%
MACs saving on small graphs and 25%~85% MACs saving on large ones. For link
prediction, GLTs lead to 48%~97% and 70% MACs saving on small and large graph
datasets, respectively, without compromising predictive performance. Codes
available at this https URL

提出了一种统一的 GNN 稀疏化 (UFG) 框架，同时剪枝图邻接矩阵和模型权重，以有效加速大规模图上的 GNN 推理，并将 lottery ticket 假设推广到 GNN，定义了图 lottery ticket (GLT) 作为核心子数据集和稀疏子网络对的一对，从原始的 GNN 和全密集图中通过迭代应用 UFS 联合鉴定。在各种 GNN 架构和不同任务的数据集上进行了实验验证，表明在不影响预测性能的前提下，GLT 能够实现小和大规模图数据集上的 MACS 节约。