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A list of awesome GNN systems.
https://github.com/chwan1016/awesome-gnn-systems
List: awesome-gnn-systems
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A list of awesome GNN systems.
- Host: GitHub
- URL: https://github.com/chwan1016/awesome-gnn-systems
- Owner: chwan1016
- Created: 2022-06-17T09:25:36.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-16T00:52:12.000Z (8 months ago)
- Last Synced: 2024-04-18T03:59:38.242Z (8 months ago)
- Topics: awesome-list, awesome-papers, graph-neural-networks, graph-systems
- Language: Python
- Homepage:
- Size: 392 KB
- Stars: 249
- Watchers: 4
- Forks: 22
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-AI-system - awesome GNN
- ultimate-awesome - awesome-gnn-systems - A list of awesome GNN systems. (Other Lists / Monkey C Lists)
README
# Awesome Graph Neural Network Systems [](https://github.com/sindresorhus/awesome)
A list of awesome systems for graph neural network (GNN). If you have any comment, please create an [issue](https://github.com/chwan1016/awesome-gnn-systems/issues) or [pull request](https://github.com/chwan1016/awesome-gnn-systems/pulls).
## Contents
- [Open Source Libraries](#open-source-libraries)
- [Papers](#papers)
- [Survey Papers](#survey-papers)
- [GNN Libraries](#gnn-libraries)
- [GNN Kernels](#gnn-kernels)
- [GNN Compilers](#gnn-compilers)
- [Distributed GNN Training Systems](#distributed-gnn-training-systems)
- [Training Systems for Scaling Graphs](#training-systems-for-scaling-graphs)
- [Quantized GNNs](#quantized-gnns)
- [GNN Dataloaders](#gnn-dataloaders)
- [GNN Training Accelerators](#gnn-training-accelerators)
- [GNN Inference Accelerators](#gnn-inference-accelerators)
- [Contribute](#contribute)
## Open Source Libraries
- [PyG: Graph Neural Network Library for PyTorch](https://github.com/rusty1s/pytorch_geometric) 
- [DGL: Python Package Built to Ease Deep Learning on Graph](https://github.com/dmlc/dgl) 
- [Graph Nets: Build Graph Nets in Tensorflow](https://github.com/deepmind/graph_nets) 
- [Euler: A Distributed Graph Deep Learning Framework](https://github.com/alibaba/euler) 
- [StellarGraph: Machine Learning on Graphs](https://github.com/stellargraph/stellargraph) 
- [Spektral: Graph Neural Networks with Keras and Tensorflow 2](https://github.com/danielegrattarola/spektral) 
- [PGL: An Efficient and Flexible Graph Learning Framework Based on PaddlePaddle](https://github.com/PaddlePaddle/PGL) 
- [CogDL: An Extensive Toolkit for Deep Learning on Graphs](https://github.com/THUDM/cogdl) 
- [DIG: A Turnkey Library for Diving into Graph Deep Learning Research](https://github.com/divelab/DIG) 
- [Jraph: A Graph Neural Network Library in Jax](https://github.com/deepmind/jraph) 
- [Graph-Learn: An Industrial Graph Neural Network Framework](https://github.com/alibaba/graph-learn) 
- [DeepGNN: a Framework for Training Machine Learning Models on Large Scale Graph Data](https://github.com/microsoft/DeepGNN) 
## Papers
### Survey Papers
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|CSUR 2024|Distributed Graph Neural Network Training: A Survey|BUPT| [[paper]](https://dl.acm.org/doi/10.1145/3648358)||
|Proceedings of the IEEE 2023|A Comprehensive Survey on Distributed Training of Graph Neural Networks|Chinese Academy of Sciences| [[paper]](https://ieeexplore.ieee.org/abstract/document/10348966)||
|arXiv 2023|A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware|UCLA| [[paper]](https://arxiv.org/abs/2306.14052)||
|arXiv 2022|Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis|ETHZ| [[paper]](https://arxiv.org/abs/2205.09702)||
|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)||
### GNN Libraries
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|JMLR 2021|DIG: A Turnkey Library for Diving into Graph Deep Learning Research|TAMU| [[paper]](https://arxiv.org/abs/2103.12608)| [[code]](https://github.com/divelab/DIG)|
|arXiv 2021|CogDL: A Toolkit for Deep Learning on Graphs|THU| [[paper]](https://arxiv.org/abs/2103.00959)| [[code]](https://github.com/THUDM/cogdl)|
|CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)| [[code]](https://github.com/danielegrattarola/spektral)|
|arXiv 2019|Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks|AWS| [[paper]](https://arxiv.org/abs/1909.01315)| [[code]](https://github.com/dmlc/dgl)|
|VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)| [[code]](https://github.com/alibaba/graph-learn)|
|arXiv 2019|Fast Graph Representation Learning with PyTorch Geometric|TU Dortmund University| [[paper]](https://arxiv.org/abs/1903.02428)| [[code]](https://github.com/rusty1s/pytorch_geometric)|
|arXiv 2018|Relational Inductive Biases, Deep Learning, and Graph Networks|DeepMind| [[paper]](https://arxiv.org/abs/1806.01261)| [[code]](https://github.com/deepmind/graph_nets)|
### GNN Kernels
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|MLSys 2022|Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective|THU| [[paper]](https://proceedings.mlsys.org/paper/2022/file/9a1158154dfa42caddbd0694a4e9bdc8-Paper.pdf)| [[code]](https://github.com/dgSPARSE/dgNN)|
|HPDC 2022|TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU|GW| [[paper]](https://dl.acm.org/doi/abs/10.1145/3502181.3531467)||
|IPDPS 2021|FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks|Indiana University Bloomington| [[paper]](https://arxiv.org/abs/2011.06391)| [[code]](https://github.com/HipGraph/FusedMM)|
|SC 2020|GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks|THU| [[paper]](https://arxiv.org/abs/2007.03179)| [[code]](https://github.com/hgyhungry/ge-spmm)|
|ICCAD 2020|fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU|UCSB| [[paper]](https://seal.ece.ucsb.edu/sites/default/files/publications/fusegcn_camera_ready_.pdf)| [[code]](https://github.com/apuaaChen/gcnLib)|
|IPDPS 2020|PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network|PKU| [[paper]](https://ieeexplore.ieee.org/document/9139807)||
### GNN Compilers
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|MLSys 2022|Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph|ShanghaiTech| [[paper]](https://proceedings.mlsys.org/paper/2022/file/a87ff679a2f3e71d9181a67b7542122c-Paper.pdf)| [[code]](https://github.com/xiezhq-hermann/graphiler)|
|EuroSys 2021|Seastar: Vertex-Centric Programming for Graph Neural Networks|CUHK| [[paper]](http://www.cse.cuhk.edu.hk/~jcheng/papers/seastar_eurosys21.pdf)||
|SC 2020|FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems|Cornell| [[paper]](https://arxiv.org/pdf/2008.11359.pdf)| [[code]](https://github.com/dglai/FeatGraph)|
### Distributed GNN Training Systems
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|arXiv 2023|Communication-Free Distributed GNN Training with Vertex Cut|Stanford| [[paper]](https://arxiv.org/pdf/2308.03209.pdf)||
|arXiv 2023|GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism|Purdue| [[paper]](https://arxiv.org/pdf/2308.10087.pdf)||
|OSDI 2023|MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms|UCSB| [[paper]](https://www.usenix.org/system/files/osdi23-wang-yuke.pdf)| [[code]](https://github.com/YukeWang96/MGG_OSDI23)|
|VLDB 2022|Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks|HKUST| [[paper]](https://www.vldb.org/pvldb/vol15/p1937-peng.pdf)| [[code]](https://github.com/chenzhao/light-dist-gnn)|
|MLSys 2022|BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling|Rice, UIUC| [[paper]](https://proceedings.mlsys.org/paper/2022/file/d1fe173d08e959397adf34b1d77e88d7-Paper.pdf)| [[code]](https://github.com/RICE-EIC/BNS-GCN)|
|MLSys 2022|Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs|Intel| [[paper]](https://arxiv.org/abs/2111.06483)| [[code]](https://github.com/IntelLabs/SAR)|
|WWW 2022|PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm|PKU| [[paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3511986)||
|ICLR 2022|PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication|Rice| [[paper]](https://openreview.net/pdf?id=kSwqMH0zn1F)| [[code]](https://github.com/RICE-EIC/PipeGCN)|
|ICLR 2022|Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks|PSU| [[paper]](https://openreview.net/pdf?id=FndDxSz3LxQ)| [[code]](https://github.com/MortezaRamezani/llcg)|
|arXiv 2021|Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2112.15345.pdf)||
|SC 2021|DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks|Intel| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480856)| [[code]](https://github.com/dmlc/dgl/pull/3024)|
|SC 2021|Efficient Scaling of Dynamic Graph Neural Networks|IBM| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480858)||
|CLUSTER 2021|2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters|NUDT| [[paper]](https://ieeexplore.ieee.org/abstract/document/9556026)||
|OSDI 2021|$P^3$: Distributed Deep Graph Learning at Scale|MSR| [[paper]](https://www.usenix.org/system/files/osdi21-gandhi.pdf)||
|OSDI 2021|Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads|UCLA| [[paper]](http://web.cs.ucla.edu/~harryxu/papers/dorylus-osdi21.pdf)| [[code]](https://github.com/uclasystem/dorylus)|
|arXiv 2021|GIST: Distributed Training for Large-Scale Graph Convolutional Networks|Rice| [[paper]](https://arxiv.org/abs/2102.10424)||
|EuroSys 2021|FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3447786.3456229)||
|EuroSys 2021|DGCL: An Efficient Communication Library for Distributed GNN Training|CUHK| [[paper]](https://dl.acm.org/doi/abs/10.1145/3447786.3456233)| [[code]](https://github.com/czkkkkkk/ragdoll)|
|SC 2020|Reducing Communication in Graph Neural Network Training|UC Berkeley| [[paper]](https://arxiv.org/pdf/2005.03300.pdf)| [[code]](https://github.com/PASSIONLab/CAGNET)|
|VLDB 2020|G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs|NUS| [[paper]](http://www.vldb.org/pvldb/vol13/p2813-liu.pdf)| [[code]](https://github.com/Xtra-Computing/G3)|
|IA3 2020|DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2010.05337.pdf)| [[code]](https://github.com/dmlc/dgl/tree/master/python/dgl/distributed)|
|MLSys 2020|Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc|Stanford| [[paper]](https://proceedings.mlsys.org/paper/2020/file/fe9fc289c3ff0af142b6d3bead98a923-Paper.pdf)| [[code]](https://github.com/jiazhihao/ROC)|
|arXiv 2020|AGL: A Scalable System for Industrial-purpose Graph Machine Learning|Ant Financial Services Group| [[paper]](https://arxiv.org/abs/2003.02454)||
|ATC 2019|NeuGraph: Parallel Deep Neural Network Computation on Large Graphs|PKU| [[paper]](https://www.usenix.org/system/files/atc19-ma_0.pdf)||
### Training Systems for Scaling Graphs
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|DaMoN 2024|In situ neighborhood sampling for large-scale GNN training|Boston University| [[paper]](https://dl.acm.org/doi/10.1145/3662010.3663443)| [[code]](https://github.com/CASP-Systems-BU/damon24-gnn-in-situ-sampling/)|
|HPCA 2024|BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing|UCLA| [[paper]](https://ieeexplore.ieee.org/abstract/document/10476427)||
|EuroSys 2023|MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks|UW–Madison| [[paper]](https://arxiv.org/abs/2202.02365)| [[code]](https://github.com/marius-team/marius)|
|VLDB 2022|ByteGNN: Efficient Graph Neural Network Training at Large Scale|ByteDance| [[paper]](https://dl.acm.org/doi/abs/10.14778/3514061.3514069)||
|VLDB 2022|Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching|Seoul National University| [[paper]](https://dl.acm.org/doi/10.14778/3551793.3551819)| [[code]](https://github.com/SNU-ARC/Ginex)|
|ISCA 2022|SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures|KAIST| [[paper]](https://dl.acm.org/doi/10.1145/3470496.3527391)||
|ICML 2022|GraphFM: Improving Large-Scale GNN Training via Feature Momentum|TAMU| [[paper]](https://arxiv.org/abs/2206.07161)| [[code]](https://github.com/divelab/DIG/tree/dig-stable/dig/lsgraph)|
|ICML 2021|GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings|TU Dortmund University| [[paper]](https://arxiv.org/abs/2106.05609)| [[code]](https://github.com/rusty1s/pyg_autoscale)|
|OSDI 2021|GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs|UCSB| [[paper]](https://www.usenix.org/system/files/osdi21-wang-yuke.pdf)| [[code]](https://github.com/YukeWang96/OSDI21_AE)|
### Quantized GNNs
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|Neurocomputing 2022|EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization|ZJU| [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231222008293)| [[code]](https://github.com/Lyun-Huang/EPQuant)|
|ICLR 2022|EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression|Rice| [[paper]](https://openreview.net/pdf?id=vkaMaq95_rX)| [[code]](https://github.com/warai-0toko/Exact)|
|PPoPP 2022|QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core|UCSB| [[paper]](https://arxiv.org/abs/2111.09547)| [[code]](https://github.com/YukeWang96/PPoPP22_QGTC)|
|CVPR 2021|Binary Graph Neural Networks|ICL| [[paper]](https://arxiv.org/abs/2012.15823)| [[code]](https://github.com/mbahri/binary_gnn)|
|CVPR 2021|Bi-GCN: Binary Graph Convolutional Network|Beihang University| [[paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html)| [[code]](https://github.com/bywmm/Bi-GCN)|
|EuroMLSys 2021|Learned Low Precision Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2009.09232)||
|World Wide Web 2021|Binarized Graph Neural Network|UTS| [[paper]](https://arxiv.org/pdf/2004.11147.pdf)||
|ICLR 2021|Degree-Quant: Quantization-Aware Training for Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2008.05000)| [[code]](https://github.com/camlsys/degree-quant)|
|ICTAI 2020|SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization|UCSB| [[paper]](https://ieeexplore.ieee.org/abstract/document/9288186/)| [[code]](https://github.com/YukeWang96/SGQuant)|
### GNN Dataloaders
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|NSDI 2023|BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing|ByteDance| [[paper]](https://arxiv.org/abs/2112.08541)||
|MLSys 2022|Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining|MIT| [[paper]](https://proceedings.mlsys.org/paper/2022/file/35f4a8d465e6e1edc05f3d8ab658c551-Paper.pdf)| [[code]](https://github.com/MITIBMxGraph/SALIENT)|
|EuroSys 2022|GNNLab: A Factored System for Sample-based GNN Training over GPUs|SJTU| [[paper]](https://dl.acm.org/doi/abs/10.1145/3492321.3519557)| [[code]](https://github.com/SJTU-IPADS/gnnlab)|
|KDD 2021|Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs|UCLA| [[paper]](https://arxiv.org/pdf/2106.06150.pdf)||
|PPoPP 2021|Understanding and Bridging the Gaps in Current GNN Performance Optimizations|THU| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3437801.3441585)| [[code]](https://github.com/xxcclong/GNN-Computing)|
|VLDB 2021|Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture|UIUC| [[paper]](https://arxiv.org/abs/2103.03330)| [[code]](https://github.com/K-Wu/pytorch-direct_dgl)|
|TPDS 2021|Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs|USTC| [[paper]](https://gnnsys.github.io/papers/GNNSys21_paper_8.pdf)| [[code]](https://github.com/zhiqi-0/PaGraph)|
|SoCC 2020|PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching|USTC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3419111.3421281)| [[code]](https://github.com/zhiqi-0/PaGraph)|
|arXiv 2019|TigerGraph: A Native MPP Graph Database|UCSD| [[paper]](https://arxiv.org/pdf/1901.08248.pdf)||
### GNN Training Accelerators
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|ISCA 2022|Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques|UIUC| [[paper]](http://iacoma.cs.uiuc.edu/iacoma-papers/isca22.pdf)||
|ISCA 2022|Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network|Alibaba| [[paper]](https://dl.acm.org/doi/abs/10.1145/3470496.3527439)||
|arXiv 2021|GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing|PKU| [[paper]](https://arxiv.org/abs/2111.00680)||
|DATE 2021|ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks|WSU| [[paper]](https://arxiv.org/abs/2102.07959)||
|TCAD 2021|Rubik: A Hierarchical Architecture for Efficient Graph Learning|Chinese Academy of Sciences| [[paper]](https://arxiv.org/pdf/2009.12495.pdf)||
|FPGA 2020|GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms|USC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3373087.3375312)| [[code]](https://github.com/GraphSAINT/GraphACT)|
### GNN Inference Accelerators
| Venue | Title | Affiliation | Link | Source |
| :---: | :---: | :---------: | :---: | :----: |
|JAIHC 2022|DRGN: a dynamically reconfigurable accelerator for graph neural networks|XJTU| [[paper]](https://link.springer.com/article/10.1007/s12652-022-04402-x)||
|DAC 2022|GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching|UMN| [[paper]](https://arxiv.org/abs/2105.10554)||
|IPDPS 2022|Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators|GaTech| [[paper]](https://arxiv.org/abs/2103.07977)| [[code]](https://github.com/stonne-simulator/omega)|
|arXiv 2022|FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming|GaTech| [[paper]](https://arxiv.org/abs/2204.13103)||
|CICC 2022|StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing|UCLA| [[paper]](https://web.cs.ucla.edu/~atefehsz/publication/StreamGCN-CICC22.pdf)||
|HPCA 2022|Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures|HUST| [[paper]](https://ieeexplore.ieee.org/document/9773267)||
|HPCA 2022|GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design|Rice, PNNL| [[paper]](https://arxiv.org/abs/2112.11594)| [[code]](https://github.com/RICE-EIC/GCoD)|
|arXiv 2022|GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration|GaTech| [[paper]](https://arxiv.org/abs/2201.08475)||
|DAC 2021|DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks|Hunan University| [[paper]](https://ieeexplore.ieee.org/document/9586298)||
|DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)||
|DAC 2021|TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning|Chinese Academy of Sciences| [[paper]](https://ieeexplore.ieee.org/abstract/document/9586193)||
|ICCAD 2021|G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency|Rice| [[paper]](https://arxiv.org/abs/2109.08983)||
|MICRO 2021|I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization|PNNL| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3466752.3480113)||
|arXiv 2021|ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration|SJTU| [[paper]](https://arxiv.org/abs/2107.08709)||
|TComp 2021|EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks|Chinese Academy of Sciences| [[paper]](https://arxiv.org/abs/1909.00155)||
|HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)||
|APA 2020|GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks|PKU| [[paper]](http://115.27.240.201/docs/20200915165942122459.pdf)||
|ASAP 2020|Hardware Acceleration of Large Scale GCN Inference|USC| [[paper]](https://ieeexplore.ieee.org/document/9153263)||
|DAC 2020|Hardware Acceleration of Graph Neural Networks|UIUC| [[paper]](http://rakeshk.web.engr.illinois.edu/dac20.pdf)||
|MICRO 2020|AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing|PNNL| [[paper]](https://ieeexplore.ieee.org/abstract/document/9252000)||
|arXiv 2020|GRIP: A Graph Neural Network Accelerator Architecture|Stanford| [[paper]](https://arxiv.org/pdf/2007.13828.pdf)||
|HPCA 2020|HyGCN: A GCN Accelerator with Hybrid Architecture|UCSB| [[paper]](https://arxiv.org/pdf/2001.02514.pdf)||
## Contribute
We welcome contributions to [this repository](https://github.com/chwan1016/awesome-gnn-systems). To add new papers to this list, please update JSON files under `./res/papers/`. Our bots will update the paper list in `README.md` automatically. The citations of newly added papers will be updated within one day.