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LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator

https://doi.org/10.1145/3550075
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-06028-6_26openalex-snapshot
CiteSeer x : A Scholarly Big Dataset
inferredcites →https://doi.org/10.1007/978-3-319-93417-4_38openalex-snapshot
Modeling Relational Data with Graph Convolutional Networks
inferredcites →https://doi.org/10.1016/j.polymertesting.2003.09.013openalex-snapshot
Characterization of TPU-elastomers by thermal analysis (DSC)
inferredcites →https://doi.org/10.1093/bib/bbz042openalex-snapshot
Graph convolutional networks for computational drug development and discovery
inferredcites →https://doi.org/10.1109/access.2021.3084599openalex-snapshot
Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM
inferredcites →https://doi.org/10.1109/cvpr.2018.00097openalex-snapshot
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
inferredcites →https://doi.org/10.1109/hpca47549.2020.00012openalex-snapshot
HyGCN: A GCN Accelerator with Hybrid Architecture
inferredcites →https://doi.org/10.1109/hpca51647.2021.00070openalex-snapshot
GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/ictai50040.2020.00198openalex-snapshot
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
inferredcites →https://doi.org/10.1109/micro.2016.7783723openalex-snapshot
Cambricon-X: An accelerator for sparse neural networks
inferredcites →https://doi.org/10.1109/micro50266.2020.00079openalex-snapshot
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
inferredcites →https://doi.org/10.1109/tc.2020.3014632openalex-snapshot
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
inferredcites →https://doi.org/10.1109/tcad.2005.852293openalex-snapshot
Power modeling and characteristics of field programmable gate arrays
inferredcites →https://doi.org/10.1109/tcad.2021.3052138openalex-snapshot
Cambricon-G: A Polyvalent Energy-Efficient Accelerator for Dynamic Graph Neural Networks
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tvlsi.2019.2939726openalex-snapshot
OPU: An FPGA-Based Overlay Processor for Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/3007787.3001163openalex-snapshot
EIE
inferredcites →https://doi.org/10.1145/3007787.3001179openalex-snapshot
Cambricon
inferredcites →https://doi.org/10.1145/3219819.3219890openalex-snapshot
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
inferredcites →https://doi.org/10.1145/3292500.3330925openalex-snapshot
Cluster-GCN
inferredcites →https://doi.org/10.1145/3373087.3375311openalex-snapshot
Light-OPU
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.sea.2024.20openalex-snapshot
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.02216openalex-snapshot
Inductive Representation Learning on Large Graphs
inferredcites →https://doi.org/10.48550/arxiv.1710.06071openalex-snapshot
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
inferredcites →https://doi.org/10.48550/arxiv.1803.03735openalex-snapshot
Attention-based Graph Neural Network for Semi-supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1905.13686openalex-snapshot
Explainability Techniques for Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1907.04931openalex-snapshot
GraphSAINT: Graph Sampling Based Inductive Learning Method
inferredcites →https://doi.org/10.48550/arxiv.2009.12495openalex-snapshot
Rubik: A Hierarchical Architecture for Efficient Graph Learning
inferredcites →https://doi.org/10.5194/os-9-1-2013openalex-snapshot
The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-62874-0_9openalex-snapshot
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-Based Vision
inferred← citeshttps://doi.org/10.1007/978-981-95-8411-6_2openalex-snapshot
FPGA-Based Hybrid GCN–Transformer Accelerator for 3D Pose Estimation
inferred← citeshttps://doi.org/10.1016/j.jer.2025.06.005openalex-snapshot
SSM-GCN: An FPGA-based efficient GCN accelerator for symmetric sparse matrices
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.02.032openalex-snapshot
Software-hardware co-design for accelerating large-scale graph convolutional network inference on FPGA
inferred← citeshttps://doi.org/10.1016/j.sysarc.2023.102924openalex-snapshot
VIDGCN: Embracing input data diversity with a configurable graph convolutional network accelerator
inferred← citeshttps://doi.org/10.1109/fpl57034.2022.00073openalex-snapshot
SkeletonGCN: A Simple Yet Effective Accelerator For GCN Training
inferred← citeshttps://doi.org/10.1109/fpl60245.2023.00039openalex-snapshot
Graph-OPU: A Highly Integrated FPGA-Based Overlay Processor for Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/fpl64840.2024.00034openalex-snapshot
A Software-Programmable Neural Processing Unit for Graph Neural Network Inference on FPGAs
inferred← citeshttps://doi.org/10.1109/fpl68686.2025.00019openalex-snapshot
Multiplexer Optimizations for Virtex FPGAs
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11464614openalex-snapshot
A Conflict-Free SpDMM Accelerator for GCN Inference on FPGA
inferred← citeshttps://doi.org/10.1109/icc45041.2023.10278567openalex-snapshot
g-BERT: Enabling Green BERT Deployment on FPGA via Hardware-Aware Hybrid Pruning
inferred← citeshttps://doi.org/10.1109/icc45041.2023.10279108openalex-snapshot
microGEMM: An Effective CNN-Based Inference Acceleration for Edge Computing
inferred← citeshttps://doi.org/10.1109/iscas46773.2023.10181406openalex-snapshot
Efficient Implementation of Activation Function on FPGA for Accelerating Neural Networks
inferred← citeshttps://doi.org/10.1109/iscas46773.2023.10181582openalex-snapshot
Edge FPGA-based Onsite Neural Network Training
inferred← citeshttps://doi.org/10.1109/iscas48785.2022.9937817openalex-snapshot
Customizable FPGA-based Accelerator for Binarized Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/nana63151.2024.00069openalex-snapshot
LightGCN-LSC: Lightweight Graph Convolution Network for Learning Style Classification in Online Education
inferred← citeshttps://doi.org/10.1109/tpds.2025.3632073openalex-snapshot
DAHBM-GCN: A Flexible Graph Convolution Network Accelerator With Multiple Dataflows and HBM
inferred← citeshttps://doi.org/10.1109/tsusc.2023.3313880openalex-snapshot
Morph-GCNX: A Universal Architecture for High-Performance and Energy-Efficient Graph Convolutional Network Acceleration
inferred← citeshttps://doi.org/10.1109/tsusc.2025.3577218openalex-snapshot
GreeNX: An Energy-Efficient and Sustainable Approach to Sparse Graph Convolution Networks Accelerators Using DVFS
inferred← citeshttps://doi.org/10.1109/tvlsi.2026.3706375openalex-snapshot
SPP-GCN: An Efficient GCN Accelerator Under Low Memory Footprint
inferred← citeshttps://doi.org/10.1145/3676536.3676761openalex-snapshot
ChatOPU: An FPGA-based Overlay Processor for Large Language Models with Unstructured Sparsity
inferred← citeshttps://doi.org/10.1145/3686490.3686539openalex-snapshot
A Review of FPGA-based Graph Neural Network Accelerator Architectures
inferred← citeshttps://doi.org/10.1145/3687480openalex-snapshot
FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-Art to Future Opportunities
inferred← citeshttps://doi.org/10.1145/3691636openalex-snapshot
Graph-OPU: A Highly Flexible FPGA-Based Overlay Processor for Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3776568openalex-snapshot
TernaryGNNs: A High-Throughput, Area-Efficient Ternary Weight GNNs Inference Framework on CPU-FPGA Heterogeneous Platform
inferred← citeshttps://doi.org/10.1186/s40537-024-01022-4openalex-snapshot
A survey of graph convolutional networks (GCNs) in FPGA-based accelerators
inferred← citeshttps://doi.org/10.1360/ssi-2024-0351openalex-snapshot
FPGA Overlay processor for AI computing
inferred← citeshttps://doi.org/10.2139/ssrn.6225887openalex-snapshot
Inference Acceleration for Graph Convolutional Networks on RISC-V Systems
inferred← citeshttps://doi.org/10.23919/cje.2023.00.135openalex-snapshot
A Survey on Graph Neural Network Acceleration: A Hardware Perspective
inferred← citeshttps://doi.org/10.3390/app12147246openalex-snapshot
Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification
inferred← citeshttps://doi.org/10.3390/electronics11223833openalex-snapshot
GPGCN: A General-Purpose Graph Convolution Neural Network Accelerator Based on RISC-V ISA Extension
inferred← citeshttps://doi.org/10.3390/electronics15143113openalex-snapshot
A Hardware-Oriented Federated GNN Approach for Wireless Localization and Misuser Detection
inferred← citeshttps://doi.org/10.3390/info15070377openalex-snapshot
A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems
inferred← citeshttps://doi.org/10.7287/peerj-cs.1166v0.1/reviews/1openalex-snapshot
Peer Review #1 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1166v0.1/reviews/2openalex-snapshot
Peer Review #2 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1166v0.2/reviews/1openalex-snapshot
Peer Review #1 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1166v0.2/reviews/2openalex-snapshot
Peer Review #2 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.1166openalex-snapshot
A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities
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