GNNHLS: Evaluating Graph Neural Network Inference via High-Level
  Synthesis
Chenfeng Zhao Zehao Dong Yixin Chen Xuan Zhang Roger D. Chamberlain
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.