mlsanityData to 14 Oct 2026

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

arXiv 2309.16022 ↗First posted 27 Sep 2023cs.LG cs.AR cs.PFcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 27 Sep 2023.

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15 of 29 references.

References · 15

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3 more
  1. 2018Transformations of High-Level Synthesis Codes for High-Performance Computingcited by 17
  2. 2022GenGNN: A Generic FPGA Framework for Graph Neural Network Accelerationcited by 9
  3. 2020Exploring the acceleration of Nekbone on reconfigurable architecturescited by 1

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  4. 2026Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference
  5. 2025DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System
  6. 2023Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
  7. 2024Acceleration Algorithms in GNNs: A Survey
  8. 2022Efficient Graph Neural Network Inference at Large Scale