This repo is to collect the state-of-the-art GNN hardware acceleration paper
PYG: Fast Graph Representation Learning with PyTorch Geometric [PDF][github]
DGL: Deep Graph Library (DGL), [github]
Zeng, Hanqing, and Viktor Prasanna. "Graphact: Accelerating gcn training on cpu-fpga heterogeneous platforms." The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. 2020 [PDF]
Zeng, Hanqing, et al. "Accurate, efficient and scalable graph embedding." 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2019. [PDF]
Zhang, Bingyi, Rajgopal Kannan, and Viktor Prasanna. "BoostGCN: A Framework for Optimizing GCN Inference on FPGA." 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2021. [PDF]
Bingyi Zhang, Hanqing Zeng and Viktor Prasanna, Hardware Acceleration of Large Scale GCN Inference, The 31st IEEE International Conference on Application-specific Systems, Architectures and Processors. [PDF]
Yan, Mingyu, et al. "Hygcn: A gcn accelerator with hybrid architecture." 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2020. [PDF]
Hwang, Ranggi, et al. "Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations." arXiv preprint arXiv:2005.05968 (2020). [PDF]
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing. [PDF]
Kiningham, Kevin, Philip Levis, and Christopher Ré. "GReTA: Hardware Optimized Graph Processing for GNNs." (2020). [PDF]
Liang, Shengwen, et al. "DeepBurning-GL: an automated framework for generating graph neural network accelerators." 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2020. [PDF]
Auten, Adam, Matthew Tomei, and Rakesh Kumar. "Hardware Acceleration of Graph Neural Networks." [PDF]
He, Lei. "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks." arXiv preprint arXiv:1909.00155 (2019). [PDF]
Srivastava, Nitish, et al. "Tensaurus: A Versatile Accelerator for Mixed Sparse-Dense Tensor Computations." 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2020. [PDF]
Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang, GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks, International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'20) [PDF]
C. Tian, L. Ma, Z. Yang and Y. Dai, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network," 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2020, pp. 936-945, doi: 10.1109/IPDPS47924.2020.00100 [PDF]
GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs [PDF]
M. Yan et al., "Characterizing and Understanding GCNs on GPU," in IEEE Computer Architecture Letters, vol. 19, no. 1, pp. 22-25, 1 Jan.-June 2020, doi: 10.1109/LCA.2020.2970395. [PDF]
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs, [PDF]