diff --git a/.github/citation/citation.json b/.github/citation/citation.json
index 46f1f2a..ec92267 100644
--- a/.github/citation/citation.json
+++ b/.github/citation/citation.json
@@ -1 +1 @@
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update": "2024-11-21"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 53, "last update": "2024-11-21"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 23, "last update": "2024-11-21"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 50, "last update": "2024-11-21"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 3, "last update": "2024-11-21"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 37, "last update": "2024-11-21"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 36, "last update": "2024-11-21"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 32, "last update": "2024-11-21"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 14, "last update": "2024-11-21"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 347, "last update": "2024-11-21"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 17, "last update": "2024-11-21"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 95, "last update": "2024-11-21"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 60, "last update": "2024-11-21"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 32, "last update": "2024-11-21"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 99, "last update": "2024-11-21"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 10, "last update": "2024-11-21"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 12, "last update": "2024-11-21"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 35, "last update": "2024-11-21"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 14, "last update": "2024-11-21"}, "Binary Graph Neural Networks": {"citation": 57, "last update": "2024-11-22"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 45, "last update": "2024-11-22"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 155, "last update": "2024-11-22"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 56, "last update": "2024-11-22"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 52, "last update": "2024-11-22"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 128, "last update": "2024-11-22"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 125, "last update": "2024-11-22"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 24, "last update": "2024-11-22"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 126, "last update": "2024-11-22"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 98, "last update": "2024-11-22"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 44, "last update": "2024-11-22"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 9, "last update": "2024-11-22"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 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Network Acceleration: Algorithms, Systems, and Customized Hardware": {"citation": 22, "last update": "2024-11-23"}, "A Comprehensive Survey on Distributed Training of Graph Neural Networks": {"citation": 29, "last update": "2024-11-23"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 45, "last update": "2024-11-23"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 37, "last update": "2024-11-23"}, "Learned Low Precision Graph Neural Networks": {"citation": 39, "last update": "2024-11-23"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 17, "last update": "2024-11-23"}, "Communication-Free Distributed GNN Training with Vertex Cut": {"citation": 1, "last update": "2024-11-23"}, "MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks": {"citation": 35, "last update": "2024-11-23"}, "Improving the Accuracy, Scalability, and Performance of 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5, "last update": "2024-11-23"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 140, "last update": "2024-11-23"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 136, "last update": "2024-11-23"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 52, "last update": "2024-11-23"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 2, "last update": "2024-11-23"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 316, "last update": "2024-11-23"}, "BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing": {"citation": 3, "last update": "2024-11-23"}, "In situ neighborhood sampling for large-scale GNN training": {"citation": 0, "last update": "2024-11-23"}}
\ No newline at end of file
+{"TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 14, "last update": "2024-11-21"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 53, "last update": "2024-11-21"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 23, "last update": "2024-11-21"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 50, "last update": "2024-11-21"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 3, "last update": "2024-11-21"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 37, "last update": "2024-11-21"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 36, "last update": "2024-11-21"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware 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update": "2024-11-21"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 12, "last update": "2024-11-21"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 35, "last update": "2024-11-21"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 14, "last update": "2024-11-21"}, "Binary Graph Neural Networks": {"citation": 57, "last update": "2024-11-22"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 45, "last update": "2024-11-22"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 155, "last update": "2024-11-22"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 56, "last update": "2024-11-22"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 52, "last update": "2024-11-22"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 128, "last update": "2024-11-22"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 125, "last update": "2024-11-22"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 24, "last update": "2024-11-22"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 126, "last update": "2024-11-22"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 98, "last update": "2024-11-22"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 44, "last update": "2024-11-22"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 9, "last update": "2024-11-22"}, "Understanding and 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"2024-11-22"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 76, "last update": "2024-11-22"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 293, "last update": "2024-11-23"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 161, "last update": "2024-11-23"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 32, "last update": "2024-11-23"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 70, "last update": "2024-11-23"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 193, "last update": "2024-11-23"}, "Reducing Communication in Graph Neural Network Training": {"citation": 117, "last update": "2024-11-23"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 23, "last update": "2024-11-23"}, "A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware": {"citation": 22, "last update": "2024-11-23"}, "A Comprehensive Survey on Distributed Training of Graph Neural Networks": {"citation": 29, "last update": "2024-11-23"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 45, "last update": "2024-11-23"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 37, "last update": "2024-11-23"}, "Learned Low Precision Graph Neural Networks": {"citation": 39, "last update": "2024-11-23"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 17, "last update": "2024-11-23"}, "Communication-Free Distributed GNN Training with Vertex Cut": {"citation": 1, "last update": "2024-11-23"}, "MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks": {"citation": 35, "last update": 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for General and Scalable Graph Neural Network Acceleration": {"citation": 5, "last update": "2024-11-23"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 140, "last update": "2024-11-23"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 136, "last update": "2024-11-23"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 52, "last update": "2024-11-23"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 2, "last update": "2024-11-23"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 316, "last update": "2024-11-23"}, "BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing": {"citation": 3, "last update": "2024-11-23"}, "In situ neighborhood sampling for large-scale GNN training": {"citation": 0, "last update": "2024-11-23"}, "Ginex: SSD-enabled Billion-scale Graph 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Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 77, "last update": "2024-11-25"}, "Binarized Graph Neural Network": {"citation": 33, "last update": "2024-11-25"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 17, "last update": "2024-11-25"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 81, "last update": "2024-11-25"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 6, "last update": "2024-11-25"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 48, "last update": "2024-11-25"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 157, "last update": "2024-11-25"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 52, "last update": "2024-11-25"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 71, "last update": "2024-11-25"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 12, "last update": "2024-11-25"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 317, "last update": "2024-11-25"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 26, "last update": "2024-11-25"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 57, "last update": "2024-11-25"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 86, "last update": "2024-11-25"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 34, "last update": "2024-11-25"}}
\ No newline at end of file
diff --git a/README.md b/README.md
index fd3d92b..6ac0a37 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 | :---: | :---: | :---------: | :---: | :----: |
 |JMLR 2021|DIG: A Turnkey Library for Diving into Graph Deep Learning Research|TAMU| [[paper]](https://arxiv.org/abs/2103.12608)![Scholar citations](https://img.shields.io/badge/Citations-98-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/divelab/DIG)![GitHub stars](https://img.shields.io/github/stars/divelab/DIG.svg?logo=github&label=Stars)|
 |arXiv 2021|CogDL: A Toolkit for Deep Learning on Graphs|THU| [[paper]](https://arxiv.org/abs/2103.00959)![Scholar citations](https://img.shields.io/badge/Citations-2-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/THUDM/cogdl)![GitHub stars](https://img.shields.io/github/stars/THUDM/cogdl.svg?logo=github&label=Stars)|
-|CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)![Scholar citations](https://img.shields.io/badge/Citations-315-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/danielegrattarola/spektral)![GitHub stars](https://img.shields.io/github/stars/danielegrattarola/spektral.svg?logo=github&label=Stars)|
+|CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)![Scholar citations](https://img.shields.io/badge/Citations-317-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/danielegrattarola/spektral)![GitHub stars](https://img.shields.io/github/stars/danielegrattarola/spektral.svg?logo=github&label=Stars)|
 |arXiv 2019|Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks|AWS| [[paper]](https://arxiv.org/abs/1909.01315)![Scholar citations](https://img.shields.io/badge/Citations-1.3k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl)![GitHub stars](https://img.shields.io/github/stars/dmlc/dgl.svg?logo=github&label=Stars)|
 |VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)![Scholar citations](https://img.shields.io/badge/Citations-316-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/alibaba/graph-learn)![GitHub stars](https://img.shields.io/github/stars/alibaba/graph-learn.svg?logo=github&label=Stars)|
 |arXiv 2019|Fast Graph Representation Learning with PyTorch Geometric|TU Dortmund University| [[paper]](https://arxiv.org/abs/1903.02428)![Scholar citations](https://img.shields.io/badge/Citations-4.9k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/rusty1s/pytorch_geometric)![GitHub stars](https://img.shields.io/github/stars/rusty1s/pytorch_geometric.svg?logo=github&label=Stars)|
@@ -52,7 +52,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 | :---: | :---: | :---------: | :---: | :----: |
 |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)![Scholar citations](https://img.shields.io/badge/Citations-50-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dgSPARSE/dgNN)![GitHub stars](https://img.shields.io/github/stars/dgSPARSE/dgNN.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-21-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|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)![Scholar citations](https://img.shields.io/badge/Citations-47-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/HipGraph/FusedMM)![GitHub stars](https://img.shields.io/github/stars/HipGraph/FusedMM.svg?logo=github&label=Stars)|
+|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)![Scholar citations](https://img.shields.io/badge/Citations-48-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/HipGraph/FusedMM)![GitHub stars](https://img.shields.io/github/stars/HipGraph/FusedMM.svg?logo=github&label=Stars)|
 |SC 2020|GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks|THU| [[paper]](https://arxiv.org/abs/2007.03179)![Scholar citations](https://img.shields.io/badge/Citations-125-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/hgyhungry/ge-spmm)![GitHub stars](https://img.shields.io/github/stars/hgyhungry/ge-spmm.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-23-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/apuaaChen/gcnLib)![GitHub stars](https://img.shields.io/github/stars/apuaaChen/gcnLib.svg?logo=github&label=Stars)|
 |IPDPS 2020|PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network|PKU| [[paper]](https://ieeexplore.ieee.org/document/9139807)![Scholar citations](https://img.shields.io/badge/Citations-47-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
@@ -69,10 +69,10 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |arXiv 2023|GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism|Purdue| [[paper]](https://arxiv.org/pdf/2308.10087.pdf)![Scholar citations](https://img.shields.io/badge/Citations-1-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-22-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/MGG_OSDI23)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/MGG_OSDI23.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-66-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/chenzhao/light-dist-gnn)![GitHub stars](https://img.shields.io/github/stars/chenzhao/light-dist-gnn.svg?logo=github&label=Stars)|
-|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)![Scholar citations](https://img.shields.io/badge/Citations-76-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/BNS-GCN)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/BNS-GCN.svg?logo=github&label=Stars)|
+|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)![Scholar citations](https://img.shields.io/badge/Citations-77-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/BNS-GCN)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/BNS-GCN.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-24-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/IntelLabs/SAR)![GitHub stars](https://img.shields.io/github/stars/IntelLabs/SAR.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|ICLR 2022|PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication|Rice| [[paper]](https://openreview.net/pdf?id=kSwqMH0zn1F)![Scholar citations](https://img.shields.io/badge/Citations-69-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/PipeGCN)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/PipeGCN.svg?logo=github&label=Stars)|
+|ICLR 2022|PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication|Rice| [[paper]](https://openreview.net/pdf?id=kSwqMH0zn1F)![Scholar citations](https://img.shields.io/badge/Citations-71-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/PipeGCN)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/PipeGCN.svg?logo=github&label=Stars)|
 |ICLR 2022|Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks|PSU| [[paper]](https://openreview.net/pdf?id=FndDxSz3LxQ)![Scholar citations](https://img.shields.io/badge/Citations-34-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/MortezaRamezani/llcg)![GitHub stars](https://img.shields.io/github/stars/MortezaRamezani/llcg.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-36-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |SC 2021|DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks|Intel| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480856)![Scholar citations](https://img.shields.io/badge/Citations-126-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl/pull/3024)|
@@ -81,7 +81,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |OSDI 2021|$P^3$: Distributed Deep Graph Learning at Scale|MSR| [[paper]](https://www.usenix.org/system/files/osdi21-gandhi.pdf)![Scholar citations](https://img.shields.io/badge/Citations-157-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-148-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/uclasystem/dorylus)![GitHub stars](https://img.shields.io/github/stars/uclasystem/dorylus.svg?logo=github&label=Stars)|
 |arXiv 2021|GIST: Distributed Training for Large-Scale Graph Convolutional Networks|Rice| [[paper]](https://arxiv.org/abs/2102.10424)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|EuroSys 2021|FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3447786.3456229)![Scholar citations](https://img.shields.io/badge/Citations-64-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|EuroSys 2021|FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3447786.3456229)![Scholar citations](https://img.shields.io/badge/Citations-65-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |EuroSys 2021|DGCL: An Efficient Communication Library for Distributed GNN Training|CUHK| [[paper]](https://dl.acm.org/doi/abs/10.1145/3447786.3456233)![Scholar citations](https://img.shields.io/badge/Citations-91-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/czkkkkkk/ragdoll)![GitHub stars](https://img.shields.io/github/stars/czkkkkkk/ragdoll.svg?logo=github&label=Stars)|
 |SC 2020|Reducing Communication in Graph Neural Network Training|UC Berkeley| [[paper]](https://arxiv.org/pdf/2005.03300.pdf)![Scholar citations](https://img.shields.io/badge/Citations-117-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/PASSIONLab/CAGNET)![GitHub stars](https://img.shields.io/github/stars/PASSIONLab/CAGNET.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Xtra-Computing/G3)![GitHub stars](https://img.shields.io/github/stars/Xtra-Computing/G3.svg?logo=github&label=Stars)|
@@ -97,28 +97,28 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |EuroSys 2023|MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks|UW–Madison| [[paper]](https://arxiv.org/abs/2202.02365)![Scholar citations](https://img.shields.io/badge/Citations-35-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/marius-team/marius)![GitHub stars](https://img.shields.io/github/stars/marius-team/marius.svg?logo=github&label=Stars)|
 |VLDB 2022|ByteGNN: Efficient Graph Neural Network Training at Large Scale|ByteDance| [[paper]](https://dl.acm.org/doi/abs/10.14778/3514061.3514069)![Scholar citations](https://img.shields.io/badge/Citations-76-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-27-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/SNU-ARC/Ginex)![GitHub stars](https://img.shields.io/github/stars/SNU-ARC/Ginex.svg?logo=github&label=Stars)|
-|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)![Scholar citations](https://img.shields.io/badge/Citations-43-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|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)![Scholar citations](https://img.shields.io/badge/Citations-44-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |ICML 2022|GraphFM: Improving Large-Scale GNN Training via Feature Momentum|TAMU| [[paper]](https://arxiv.org/abs/2206.07161)![Scholar citations](https://img.shields.io/badge/Citations-29-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[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)![Scholar citations](https://img.shields.io/badge/Citations-165-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/rusty1s/pyg_autoscale)![GitHub stars](https://img.shields.io/github/stars/rusty1s/pyg_autoscale.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-161-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/OSDI21_AE)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/OSDI21_AE.svg?logo=github&label=Stars)|
 ### 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)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Lyun-Huang/EPQuant)![GitHub stars](https://img.shields.io/github/stars/Lyun-Huang/EPQuant.svg?logo=github&label=Stars)|
+|Neurocomputing 2022|EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization|ZJU| [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231222008293)![Scholar citations](https://img.shields.io/badge/Citations-12-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Lyun-Huang/EPQuant)![GitHub stars](https://img.shields.io/github/stars/Lyun-Huang/EPQuant.svg?logo=github&label=Stars)|
 |ICLR 2022|EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression|Rice| [[paper]](https://openreview.net/pdf?id=vkaMaq95_rX)![Scholar citations](https://img.shields.io/badge/Citations-60-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/warai-0toko/Exact)![GitHub stars](https://img.shields.io/github/stars/warai-0toko/Exact.svg?logo=github&label=Stars)|
 |PPoPP 2022|QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core|UCSB| [[paper]](https://arxiv.org/abs/2111.09547)![Scholar citations](https://img.shields.io/badge/Citations-45-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/PPoPP22_QGTC)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/PPoPP22_QGTC.svg?logo=github&label=Stars)|
 |CVPR 2021|Binary Graph Neural Networks|ICL| [[paper]](https://arxiv.org/abs/2012.15823)![Scholar citations](https://img.shields.io/badge/Citations-57-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/mbahri/binary_gnn)![GitHub stars](https://img.shields.io/github/stars/mbahri/binary_gnn.svg?logo=github&label=Stars)|
 |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)![Scholar citations](https://img.shields.io/badge/Citations-57-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/bywmm/Bi-GCN)![GitHub stars](https://img.shields.io/github/stars/bywmm/Bi-GCN.svg?logo=github&label=Stars)|
 |EuroMLSys 2021|Learned Low Precision Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2009.09232)![Scholar citations](https://img.shields.io/badge/Citations-39-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |World Wide Web 2021|Binarized Graph Neural Network|UTS| [[paper]](https://arxiv.org/pdf/2004.11147.pdf)![Scholar citations](https://img.shields.io/badge/Citations-33-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|ICLR 2021|Degree-Quant: Quantization-Aware Training for Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2008.05000)![Scholar citations](https://img.shields.io/badge/Citations-192-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/camlsys/degree-quant)![GitHub stars](https://img.shields.io/github/stars/camlsys/degree-quant.svg?logo=github&label=Stars)|
+|ICLR 2021|Degree-Quant: Quantization-Aware Training for Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2008.05000)![Scholar citations](https://img.shields.io/badge/Citations-196-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/camlsys/degree-quant)![GitHub stars](https://img.shields.io/github/stars/camlsys/degree-quant.svg?logo=github&label=Stars)|
 |ICTAI 2020|SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization|UCSB| [[paper]](https://ieeexplore.ieee.org/abstract/document/9288186/)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/SGQuant)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/SGQuant.svg?logo=github&label=Stars)|
 ### 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)![Scholar citations](https://img.shields.io/badge/Citations-70-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-57-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/MITIBMxGraph/SALIENT)![GitHub stars](https://img.shields.io/github/stars/MITIBMxGraph/SALIENT.svg?logo=github&label=Stars)|
-|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)![Scholar citations](https://img.shields.io/badge/Citations-80-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/SJTU-IPADS/gnnlab)![GitHub stars](https://img.shields.io/github/stars/SJTU-IPADS/gnnlab.svg?logo=github&label=Stars)|
+|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)![Scholar citations](https://img.shields.io/badge/Citations-81-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/SJTU-IPADS/gnnlab)![GitHub stars](https://img.shields.io/github/stars/SJTU-IPADS/gnnlab.svg?logo=github&label=Stars)|
 |KDD 2021|Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs|UCLA| [[paper]](https://arxiv.org/pdf/2106.06150.pdf)![Scholar citations](https://img.shields.io/badge/Citations-37-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |PPoPP 2021|Understanding and Bridging the Gaps in Current GNN Performance Optimizations|THU| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3437801.3441585)![Scholar citations](https://img.shields.io/badge/Citations-83-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/xxcclong/GNN-Computing)![GitHub stars](https://img.shields.io/github/stars/xxcclong/GNN-Computing.svg?logo=github&label=Stars)|
 |VLDB 2021|Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture|UIUC| [[paper]](https://arxiv.org/abs/2103.03330)![Scholar citations](https://img.shields.io/badge/Citations-67-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/K-Wu/pytorch-direct_dgl)![GitHub stars](https://img.shields.io/github/stars/K-Wu/pytorch-direct_dgl.svg?logo=github&label=Stars)|
@@ -141,7 +141,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |DAC 2022|GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching|UMN| [[paper]](https://arxiv.org/abs/2105.10554)![Scholar citations](https://img.shields.io/badge/Citations-17-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/stonne-simulator/omega)![GitHub stars](https://img.shields.io/github/stars/stonne-simulator/omega.svg?logo=github&label=Stars)|
 |arXiv 2022|FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming|GaTech| [[paper]](https://arxiv.org/abs/2204.13103)![Scholar citations](https://img.shields.io/badge/Citations-12-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|CICC 2022|StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing|UCLA| [[paper]](https://web.cs.ucla.edu/~atefehsz/publication/StreamGCN-CICC22.pdf)![Scholar citations](https://img.shields.io/badge/Citations-5-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|CICC 2022|StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing|UCLA| [[paper]](https://web.cs.ucla.edu/~atefehsz/publication/StreamGCN-CICC22.pdf)![Scholar citations](https://img.shields.io/badge/Citations-6-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |HPCA 2022|Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures|HUST| [[paper]](https://ieeexplore.ieee.org/document/9773267)![Scholar citations](https://img.shields.io/badge/Citations-44-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |HPCA 2022|GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design|Rice, PNNL| [[paper]](https://arxiv.org/abs/2112.11594)![Scholar citations](https://img.shields.io/badge/Citations-53-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/GCoD)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/GCoD.svg?logo=github&label=Stars)|
 |arXiv 2022|GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration|GaTech| [[paper]](https://arxiv.org/abs/2201.08475)![Scholar citations](https://img.shields.io/badge/Citations-17-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
@@ -154,7 +154,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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)![Scholar citations](https://img.shields.io/badge/Citations-193-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)![Scholar citations](https://img.shields.io/badge/Citations-140-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |APA 2020|GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks|PKU| [[paper]](http://115.27.240.201/docs/20200915165942122459.pdf)![Scholar citations](https://img.shields.io/badge/Citations-23-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|ASAP 2020|Hardware Acceleration of Large Scale GCN Inference|USC| [[paper]](https://ieeexplore.ieee.org/document/9153263)![Scholar citations](https://img.shields.io/badge/Citations-84-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|ASAP 2020|Hardware Acceleration of Large Scale GCN Inference|USC| [[paper]](https://ieeexplore.ieee.org/document/9153263)![Scholar citations](https://img.shields.io/badge/Citations-86-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2020|Hardware Acceleration of Graph Neural Networks|UIUC| [[paper]](http://rakeshk.web.engr.illinois.edu/dac20.pdf)![Scholar citations](https://img.shields.io/badge/Citations-142-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |MICRO 2020|AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing|PNNL| [[paper]](https://ieeexplore.ieee.org/abstract/document/9252000)![Scholar citations](https://img.shields.io/badge/Citations-293-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |arXiv 2020|GRIP: A Graph Neural Network Accelerator Architecture|Stanford| [[paper]](https://arxiv.org/pdf/2007.13828.pdf)![Scholar citations](https://img.shields.io/badge/Citations-99-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||