diff --git a/.github/citation/citation.json b/.github/citation/citation.json
index b339e47..4a36492 100644
--- a/.github/citation/citation.json
+++ b/.github/citation/citation.json
@@ -1 +1 @@
-{"Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 78, "last update": "2023-08-29"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 79, "last update": "2023-08-29"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 201, "last update": "2023-08-29"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 138, "last update": "2023-08-29"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-08-29"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 84, "last update": "2023-08-29"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 81, "last update": "2023-08-29"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 23, "last update": "2023-08-29"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-08-29"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 218, "last update": "2023-08-29"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 57, "last update": "2023-08-29"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 17, "last update": "2023-08-29"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-08-29"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 16, "last update": "2023-08-29"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 10, "last update": "2023-08-30"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 21, "last update": "2023-08-30"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-08-30"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-08-30"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 18, "last update": "2023-08-30"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 8, "last update": "2023-08-30"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 10, "last update": "2023-08-30"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 237, "last update": "2023-08-30"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 6, "last update": "2023-08-30"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 60, "last update": "2023-08-30"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 34, "last update": "2023-08-30"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 14, "last update": "2023-08-30"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-08-30"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 65, "last update": "2023-08-30"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 7, "last update": "2023-08-30"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-08-30"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 24, "last update": "2023-08-30"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 6, "last update": "2023-08-30"}, "Binary Graph Neural Networks": {"citation": 37, "last update": "2023-08-30"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 16, "last update": "2023-08-30"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 112, "last update": "2023-08-30"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 33, "last update": "2023-08-30"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 25, "last update": "2023-08-30"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 79, "last update": "2023-08-30"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 74, "last update": "2023-08-30"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 14, "last update": "2023-08-30"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 68, "last update": "2023-08-30"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 62, "last update": "2023-08-30"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 14, "last update": "2023-08-30"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-08-31"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 49, "last update": "2023-08-31"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 19, "last update": "2023-08-31"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 21, "last update": "2023-08-31"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 9, "last update": "2023-08-31"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 29, "last update": "2023-08-31"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-08-31"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 36, "last update": "2023-08-31"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 97, "last update": "2023-08-31"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 10, "last update": "2023-08-31"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 44, "last update": "2023-08-31"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-08-31"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 86, "last update": "2023-08-31"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 51, "last update": "2023-08-31"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 27, "last update": "2023-08-31"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 192, "last update": "2023-08-31"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 97, "last update": "2023-08-31"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 9, "last update": "2023-08-31"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 17, "last update": "2023-08-31"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 131, "last update": "2023-08-31"}, "Reducing Communication in Graph Neural Network Training": {"citation": 73, "last update": "2023-08-31"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-08-31"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 778, "last update": "2023-08-31"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 2965, "last update": "2023-09-01"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2889, "last update": "2023-09-01"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 34, "last update": "2023-09-01"}, "TigerGraph: A Native MPP Graph Database": {"citation": 52, "last update": "2023-09-01"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 101, "last update": "2023-09-01"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 26, "last update": "2023-09-01"}, "Binarized Graph Neural Network": {"citation": 21, "last update": "2023-09-01"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 6, "last update": "2023-09-01"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 26, "last update": "2023-09-01"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-09-01"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-09-01"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 76, "last update": "2023-09-01"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-09-01"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 29, "last update": "2023-09-01"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 4, "last update": "2023-09-01"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 209, "last update": "2023-09-01"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 7, "last update": "2023-09-01"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 20, "last update": "2023-09-01"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 19, "last update": "2023-09-01"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 20, "last update": "2023-09-01"}, "Learned Low Precision Graph Neural Networks": {"citation": 23, "last update": "2023-09-01"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 8, "last update": "2023-09-01"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 163, "last update": "2023-09-01"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 40, "last update": "2023-09-01"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 10, "last update": "2023-09-01"}}
\ No newline at end of file
+{"G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 14, "last update": "2023-08-30"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-08-30"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 65, "last update": "2023-08-30"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 7, "last update": "2023-08-30"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-08-30"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 24, "last update": "2023-08-30"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 6, "last update": "2023-08-30"}, "Binary Graph Neural Networks": {"citation": 37, "last update": "2023-08-30"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 16, "last update": "2023-08-30"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 112, "last update": "2023-08-30"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 33, "last update": "2023-08-30"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 25, "last update": "2023-08-30"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 79, "last update": "2023-08-30"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 74, "last update": "2023-08-30"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 14, "last update": "2023-08-30"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 68, "last update": "2023-08-30"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 62, "last update": "2023-08-30"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 14, "last update": "2023-08-30"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-08-31"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 49, "last update": "2023-08-31"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 19, "last update": "2023-08-31"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 21, "last update": "2023-08-31"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 9, "last update": "2023-08-31"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 29, "last update": "2023-08-31"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-08-31"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 36, "last update": "2023-08-31"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 97, "last update": "2023-08-31"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 10, "last update": "2023-08-31"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 44, "last update": "2023-08-31"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-08-31"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 86, "last update": "2023-08-31"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 51, "last update": "2023-08-31"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 27, "last update": "2023-08-31"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 192, "last update": "2023-08-31"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 97, "last update": "2023-08-31"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 9, "last update": "2023-08-31"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 17, "last update": "2023-08-31"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 131, "last update": "2023-08-31"}, "Reducing Communication in Graph Neural Network Training": {"citation": 73, "last update": "2023-08-31"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-08-31"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 778, "last update": "2023-08-31"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 2965, "last update": "2023-09-01"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2889, "last update": "2023-09-01"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 34, "last update": "2023-09-01"}, "TigerGraph: A Native MPP Graph Database": {"citation": 52, "last update": "2023-09-01"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 101, "last update": "2023-09-01"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 26, "last update": "2023-09-01"}, "Binarized Graph Neural Network": {"citation": 21, "last update": "2023-09-01"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 6, "last update": "2023-09-01"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 26, "last update": "2023-09-01"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-09-01"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-09-01"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 76, "last update": "2023-09-01"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-09-01"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 29, "last update": "2023-09-01"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 4, "last update": "2023-09-01"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 209, "last update": "2023-09-01"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 7, "last update": "2023-09-01"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 20, "last update": "2023-09-01"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 19, "last update": "2023-09-01"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 20, "last update": "2023-09-01"}, "Learned Low Precision Graph Neural Networks": {"citation": 23, "last update": "2023-09-01"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 8, "last update": "2023-09-01"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 163, "last update": "2023-09-01"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 40, "last update": "2023-09-01"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 10, "last update": "2023-09-01"}, "Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 80, "last update": "2023-09-02"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 80, "last update": "2023-09-02"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 203, "last update": "2023-09-02"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 139, "last update": "2023-09-02"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-09-02"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 85, "last update": "2023-09-02"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 81, "last update": "2023-09-02"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 25, "last update": "2023-09-02"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-09-02"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 221, "last update": "2023-09-02"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 57, "last update": "2023-09-02"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 18, "last update": "2023-09-02"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-09-02"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 17, "last update": "2023-09-02"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 10, "last update": "2023-09-02"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 22, "last update": "2023-09-02"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-09-02"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-09-02"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 19, "last update": "2023-09-02"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 8, "last update": "2023-09-02"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 10, "last update": "2023-09-02"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 238, "last update": "2023-09-02"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 6, "last update": "2023-09-02"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 61, "last update": "2023-09-02"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 34, "last update": "2023-09-02"}}
\ No newline at end of file
diff --git a/README.md b/README.md
index 69967c1..1674bb9 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 | :---: | :---: | :---------: | :---: | :----: |
 |arXiv 2022|Distributed Graph Neural Network Training: A Survey|BUPT| [[paper]](https://arxiv.org/abs/2211.00216)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |arXiv 2022|Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis|ETHZ| [[paper]](https://arxiv.org/abs/2205.09702)![Scholar citations](https://img.shields.io/badge/Citations-16-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-138-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-139-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ### GNN Libraries
 | Venue | Title | Affiliation |       Link       |   Source   |
 | :---: | :---: | :---------: | :---: | :----: |
@@ -42,13 +42,13 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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-14-_.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-209-_.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-778-_.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-218-_.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)|
+|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-221-_.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-3.0k-_.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)|
 |arXiv 2018|Relational Inductive Biases, Deep Learning, and Graph Networks|DeepMind| [[paper]](https://arxiv.org/abs/1806.01261)![Scholar citations](https://img.shields.io/badge/Citations-2.9k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/deepmind/graph_nets)![GitHub stars](https://img.shields.io/github/stars/deepmind/graph_nets.svg?logo=github&label=Stars)|
 ### 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)![Scholar citations](https://img.shields.io/badge/Citations-21-_.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)|
+|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-22-_.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-7-_.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-27-_.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-74-_.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)|
@@ -59,7 +59,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 | :---: | :---: | :---------: | :---: | :----: |
 |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)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/xiezhq-hermann/graphiler)![GitHub stars](https://img.shields.io/github/stars/xiezhq-hermann/graphiler.svg?logo=github&label=Stars)|
 |EuroSys 2021|Seastar: Vertex-Centric Programming for Graph Neural Networks|CUHK| [[paper]](http://www.cse.cuhk.edu.hk/~jcheng/papers/seastar_eurosys21.pdf)![Scholar citations](https://img.shields.io/badge/Citations-33-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|SC 2020|FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems|Cornell| [[paper]](https://arxiv.org/pdf/2008.11359.pdf)![Scholar citations](https://img.shields.io/badge/Citations-60-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dglai/FeatGraph)![GitHub stars](https://img.shields.io/github/stars/dglai/FeatGraph.svg?logo=github&label=Stars)|
+|SC 2020|FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems|Cornell| [[paper]](https://arxiv.org/pdf/2008.11359.pdf)![Scholar citations](https://img.shields.io/badge/Citations-61-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dglai/FeatGraph)![GitHub stars](https://img.shields.io/github/stars/dglai/FeatGraph.svg?logo=github&label=Stars)|
 ### Distributed GNN Training Systems
 | Venue | Title | Affiliation |       Link       |   Source   |
 | :---: | :---: | :---------: | :---: | :----: |
@@ -68,13 +68,13 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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-14-_.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-25-_.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-29-_.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-17-_.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)|
+|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-18-_.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-10-_.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-68-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[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)![Scholar citations](https://img.shields.io/badge/Citations-7-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |CLUSTER 2021|2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters|NUDT| [[paper]](https://ieeexplore.ieee.org/abstract/document/9556026)![Scholar citations](https://img.shields.io/badge/Citations-8-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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-76-_.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-78-_.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)|
+|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-80-_.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-6-_.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-34-_.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-44-_.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)|
@@ -83,7 +83,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |IA3 2020|DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2010.05337.pdf)![Scholar citations](https://img.shields.io/badge/Citations-81-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[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)![Scholar citations](https://img.shields.io/badge/Citations-163-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/jiazhihao/ROC)![GitHub stars](https://img.shields.io/github/stars/jiazhihao/ROC.svg?logo=github&label=Stars)|
 |arXiv 2020|AGL: A Scalable System for Industrial-purpose Graph Machine Learning|Ant Financial Services Group| [[paper]](https://arxiv.org/abs/2003.02454)![Scholar citations](https://img.shields.io/badge/Citations-79-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|ATC 2019|NeuGraph: Parallel Deep Neural Network Computation on Large Graphs|PKU| [[paper]](https://www.usenix.org/system/files/atc19-ma_0.pdf)![Scholar citations](https://img.shields.io/badge/Citations-201-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|ATC 2019|NeuGraph: Parallel Deep Neural Network Computation on Large Graphs|PKU| [[paper]](https://www.usenix.org/system/files/atc19-ma_0.pdf)![Scholar citations](https://img.shields.io/badge/Citations-203-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ### Training Systems for Scaling Graphs
 | Venue | Title | Affiliation |       Link       |   Source   |
 | :---: | :---: | :---------: | :---: | :----: |
@@ -103,7 +103,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |EuroMLSys 2021|Learned Low Precision Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2009.09232)![Scholar citations](https://img.shields.io/badge/Citations-23-_.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-21-_.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-101-_.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-23-_.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)|
+|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-25-_.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   |
 | :---: | :---: | :---------: | :---: | :----: |
@@ -113,8 +113,8 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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-24-_.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-49-_.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-40-_.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)|
-|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)![Scholar citations](https://img.shields.io/badge/Citations-18-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
-|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)![Scholar citations](https://img.shields.io/badge/Citations-79-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
+|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)![Scholar citations](https://img.shields.io/badge/Citations-19-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
+|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)![Scholar citations](https://img.shields.io/badge/Citations-80-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
 |arXiv 2019|TigerGraph: A Native MPP Graph Database|UCSD| [[paper]](https://arxiv.org/pdf/1901.08248.pdf)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ### GNN Training Accelerators
 | Venue | Title | Affiliation |       Link       |   Source   |
@@ -134,7 +134,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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-7-_.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-2-_.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-14-_.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-16-_.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)|
+|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-17-_.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-6-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2021|DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks|Hunan University| [[paper]](https://ieeexplore.ieee.org/document/9586298)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)![Scholar citations](https://img.shields.io/badge/Citations-20-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
@@ -143,13 +143,13 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |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)![Scholar citations](https://img.shields.io/badge/Citations-51-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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)![Scholar citations](https://img.shields.io/badge/Citations-4-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |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-131-_.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-84-_.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-85-_.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-19-_.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-57-_.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-97-_.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-192-_.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-65-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|HPCA 2020|HyGCN: A GCN Accelerator with Hybrid Architecture|UCSB| [[paper]](https://arxiv.org/pdf/2001.02514.pdf)![Scholar citations](https://img.shields.io/badge/Citations-237-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|HPCA 2020|HyGCN: A GCN Accelerator with Hybrid Architecture|UCSB| [[paper]](https://arxiv.org/pdf/2001.02514.pdf)![Scholar citations](https://img.shields.io/badge/Citations-238-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ## 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.