AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing | Paper Citation Record

Observation 62d07f3a-6fd0-44f2-93f6-d4bc26b1e120 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Imagenet classiﬁcation with deep convolutional neural networks,

Reference 1

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Observation 34673888-fc1a-451b-a389-db239752a83a · outbound

This paper cites Recurrent neural network based language model,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Recurrent neural network based language model,

Reference 2

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Observation 1787af19-d6ff-47ba-8cba-39f52cdc5c0b · outbound

This paper cites Understanding reuse, performance, and hardware cost of dnn dataﬂow: A data-centric approach,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Understanding reuse, performance, and hardware cost of dnn dataﬂow: A data-centric approach,

Reference 3

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This paper cites MAESTRO: A data-centric approach to understand reuse, performance, and hardware cost of dnn mappings,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing MAESTRO: A data-centric approach to understand reuse, performance, and hardware cost of dnn mappings,

Reference 4

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Observation 2f14509d-a650-42e9-be7b-f531b71b30f8 · outbound

This paper cites O3BNN: An out-of-order architecture for high-performance binarized neural network inference with ﬁne-grained pruning,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing O3BNN: An out-of-order architecture for high-performance binarized neural network inference with ﬁne-grained pruning,

Reference 5

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Observation a8bb7251-c041-49a3-91b2-dcb0ed979b6e · outbound

This paper cites FPDeep: Acceleration and load balancing of CNN training on FPGA clusters,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing FPDeep: Acceleration and load balancing of CNN training on FPGA clusters,

Reference 6

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Observation 111501c0-1695-4a8e-8520-6fce5e4917fd · outbound

This paper cites BSTC: A novel binarized-soft-tensor-core design for accelerating bit- based approximated neural nets,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing BSTC: A novel binarized-soft-tensor-core design for accelerating bit- based approximated neural nets,

Reference 7

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Observation dad19a8c-f387-4677-8510-6f8f94b055b4 · outbound

This paper cites A comprehensive survey on graph neural networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A comprehensive survey on graph neural networks,

Reference 8

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Observation e2a1227e-f091-43a7-8bda-736a8afd1a7b · outbound

This paper cites FPDeep: Scalable acceleration of CNN training on deeply-pipelined FPGA clusters,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing FPDeep: Scalable acceleration of CNN training on deeply-pipelined FPGA clusters,

Reference 9

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Observation 64b794e0-3db6-4fab-b903-5d69c22f752e · outbound

This paper cites O3BNN-R: An out-of-order architecture for high-performance and regularized BNN inference,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing O3BNN-R: An out-of-order architecture for high-performance and regularized BNN inference,

Reference 10

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Observation a34a4c99-dc7a-48a3-a558-ddb3b35d6729 · outbound

This paper cites Pow- erGraph: Distributed graph-parallel computation on natural graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Pow- erGraph: Distributed graph-parallel computation on natural graphs,

Reference 11

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Observation 47cfbd5e-aa18-4343-8b7f-bceaff8475c4 · outbound

This paper cites Multilevel algorithms for partitioning power-law graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Multilevel algorithms for partitioning power-law graphs,

Reference 12

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Observation f14e3831-54a8-491f-be82-2d11ba4c40c9 · outbound

This paper cites Main-memory triangle computations for very large (sparse (power-law)) graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Main-memory triangle computations for very large (sparse (power-law)) graphs,

Reference 13

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Observation cf79c3d4-a5ee-4c26-81a4-9f8f2606c93a · outbound

This paper cites Distributed power-law graph computing: Theoretical and empirical analysis,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Distributed power-law graph computing: Theoretical and empirical analysis,

Reference 14

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Observation d8b21430-4ed5-4d41-80ca-2046f7878e8f · outbound

This paper cites A random graph model for power law graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A random graph model for power law graphs,

Reference 15

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Observation 11fdda89-f340-4391-a0ba-4075e21d0573 · outbound

This paper cites The spectra of random graphs with given expected degrees,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing The spectra of random graphs with given expected degrees,

Reference 16

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Observation 7bdd3d85-b18a-4fe7-9317-b2a6d37d4bb4 · outbound

This paper cites Search in power-law networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Search in power-law networks,

Reference 17

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Observation 7c9141e1-ebf5-4668-b984-4075b1c4ca2a · outbound

This paper cites A new model for learning in graph domains,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A new model for learning in graph domains,

Reference 18

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Observation 91f130c1-5b2f-4545-a4d4-eb624305569c · outbound

This paper cites Neural network for graphs: A contextual constructive approach,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Neural network for graphs: A contextual constructive approach,

Reference 19

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Observation 4191338d-5880-43d6-9ae5-12a22b357d6e · outbound

This paper cites The graph neural network model,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing The graph neural network model,

Reference 20

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This paper cites Gated Graph Sequence Neural Networks.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Gated Graph Sequence Neural Networks

Reference 21

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Observation 8911192c-4825-4829-918d-0b5db4b73bc2 · outbound

This paper cites Learning steady- states of iterative algorithms over graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Learning steady- states of iterative algorithms over graphs,

Reference 22

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Observation 2912e4b7-2728-4d47-a9c4-b6435a11da41 · outbound

This paper cites GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Reference 23

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Observation d778e5fa-b3cc-46e2-9a5f-241287f5de6b · outbound

This paper cites Watch your step: Learning node embeddings via graph attention,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Watch your step: Learning node embeddings via graph attention,

Reference 24

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Observation d6fae5a2-a721-4761-a3c2-5bdb59394f64 · outbound

This paper cites Large-scale learnable graph convolutional networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Large-scale learnable graph convolutional networks,

Reference 25

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Observation 6e66a5ee-a158-4c9c-bf94-8fc21e28c16d · outbound

This paper cites Deep Convolutional Networks on Graph-Structured Data.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Deep Convolutional Networks on Graph-Structured Data

Reference 26

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Observation 5f53ed9d-ebec-4eca-b319-0a45dc55e106 · outbound

This paper cites Spectral Networks and Locally Connected Networks on Graphs.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Spectral Networks and Locally Connected Networks on Graphs

Reference 27

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Observation 5d9daffa-e3b3-47dd-95af-35474fd34584 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral ﬁltering,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Convolutional neural networks on graphs with fast localized spectral ﬁltering,

Reference 28

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Observation 8805dce5-1332-41ac-badd-354e4c9344c9 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Semi-Supervised Classification with Graph Convolutional Networks

Reference 29

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Observation 37de80db-a8a2-42f5-867e-5405341904f2 · outbound

This paper cites Graph transformer networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Graph transformer networks,

Reference 30

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Observation 185aaeda-2774-4b43-a88d-142aab658019 · outbound

This paper cites HyGCN: A GCN accelerator with hybrid architecture,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing HyGCN: A GCN accelerator with hybrid architecture,

Reference 31

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Observation 81c69e5a-f417-45eb-ab40-a26067f748a9 · outbound

This paper cites Guiding Cascading Failure Search with Interpretable Graph Convolutional Network.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Reference 32

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Observation 46ce8442-6ff9-4b8d-ad6f-2a583654333b · outbound

This paper cites A graph-convolutional neural network model for the prediction of chemical reactivity,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A graph-convolutional neural network model for the prediction of chemical reactivity,

Reference 33

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Observation 1075bea4-885f-4e5e-be32-5d0172cc3c4a · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,

Reference 34

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Observation a2773331-2938-4e59-a08b-8ec4dab3d1f7 · outbound

This paper cites Modeling polypharmacy side effects with graph convolutional networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Modeling polypharmacy side effects with graph convolutional networks,

Reference 35

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Observation a4a4aae0-b760-43c3-9d90-d9b81e1d7efe · outbound

This paper cites AliGraph: A comprehensive graph neural network platform,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing AliGraph: A comprehensive graph neural network platform,

Reference 36

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Observation ed01c696-ec96-4d9b-846d-16ba5d9531dd · outbound

This paper cites IoT botnet detection approach based on PSI graph and DGCNN classiﬁer,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing IoT botnet detection approach based on PSI graph and DGCNN classiﬁer,

Reference 37

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Observation 92b6c0ae-a4fa-4883-8c6a-54236ce706c9 · outbound

This paper cites EIE: efﬁcient inference engine on compressed deep neural network,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing EIE: efﬁcient inference engine on compressed deep neural network,

Reference 38

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Observation 5467ca5c-710f-4437-9208-65fa2d4d1f46 · outbound

This paper cites Cambricon-X: An accelerator for sparse neural networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Cambricon-X: An accelerator for sparse neural networks,

Reference 39

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Observation 2d8d4bc0-6e78-4eb8-9c4d-4868ca50d2d1 · outbound

This paper cites A novel zero weight/activation-aware hardware architecture of convolutional neural network,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A novel zero weight/activation-aware hardware architecture of convolutional neural network,

Reference 40

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Observation f0a67196-babc-482b-bff5-c2e4c5040a93 · outbound

This paper cites A Systematic Survey of General Sparse Matrix-Matrix Multiplication.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Reference 41

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Observation 5aa289e9-625c-445e-bd40-8e55ee22ac3a · outbound

This paper cites Performance-portable sparse matrix-matrix multiplication for many-core architectures,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Performance-portable sparse matrix-matrix multiplication for many-core architectures,

Reference 42

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Observation 9b3b0fdb-bf24-4331-89fa-0833e90071bb · outbound

This paper cites Performance-aware model for sparse matrix-matrix multiplication on the sunway taihulight supercomputer,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Performance-aware model for sparse matrix-matrix multiplication on the sunway taihulight supercomputer,

Reference 43

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Observation ecf0d5b7-7e1b-4e8d-a7ab-fed7183e6a82 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Fast Graph Representation Learning with PyTorch Geometric

Reference 44

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Observation 58518e7b-61a0-4f61-bf7c-a43488011c1f · outbound

This paper cites SCNN: An accelerator for compressed-sparse convolutional neural networks,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing SCNN: An accelerator for compressed-sparse convolutional neural networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:47.028572Z

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Observation b0732d09-1d15-4ffe-8c82-e62e47a54dee · outbound

This paper cites Dual graph convolutional networks for graph- based semi-supervised classiﬁcation,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Dual graph convolutional networks for graph- based semi-supervised classiﬁcation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:47.010850Z

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Observation ce1681d2-1857-447c-a3ed-ba3a3c39b059 · outbound

This paper cites Stochastic Training of Graph Convolutional Networks with Variance Reduction.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Stochastic Training of Graph Convolutional Networks with Variance Reduction

Reference 47

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Observation d5257dfa-dbcc-40f6-96b4-c5530b0a4378 · outbound

This paper cites Measuring the gap between FPGAs and ASICs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Measuring the gap between FPGAs and ASICs,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.992568Z

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Observation 274bab17-dde0-49e6-84a4-4466213979f4 · outbound

This paper cites Cnvlutin: Ineffectual-neuron-free deep neural network computing,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Cnvlutin: Ineffectual-neuron-free deep neural network computing,

Reference 49

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Observation 92987055-11ea-45dd-98c4-240569189315 · outbound

This paper cites Packing sparse convolutional neural networks for efﬁcient systolic array implementations: Column combining under joint optimization,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Packing sparse convolutional neural networks for efﬁcient systolic array implementations: Column combining under joint optimization,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.963489Z

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Observation e947a935-a56b-4049-8f3c-dd598fa5a33e · outbound

This paper cites Eyeriss: An energy- efﬁcient reconﬁgurable accelerator for deep convolutional neural net- works,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Eyeriss: An energy- efﬁcient reconﬁgurable accelerator for deep convolutional neural net- works,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.918813Z

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Observation e723a371-abcf-4470-b64a-b250a2e67780 · outbound

This paper cites CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices,

Reference 52

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This paper cites Sparse matrix-vector multiplication on fpgas,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Sparse matrix-vector multiplication on fpgas,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.828546Z

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This paper cites OuterSPACE: An outer product based sparse matrix multiplication accelerator,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing OuterSPACE: An outer product based sparse matrix multiplication accelerator,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.801291Z

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Observation 70f4c383-7b65-4134-a5bb-510c4f4b4f7e · outbound

This paper cites SIGMA: A sparse and irregular gemm accelerator with ﬂexible interconnects for dnn training,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing SIGMA: A sparse and irregular gemm accelerator with ﬂexible interconnects for dnn training,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.783201Z

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This paper cites ALRESCHA: A lightweight reconﬁgurable sparse-computation accel- erator,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing ALRESCHA: A lightweight reconﬁgurable sparse-computation accel- erator,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.766137Z

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Observation 851251bd-ee38-4b11-b360-ad8848abf569 · outbound

This paper cites GraphR: Accelerating graph processing using ReRAM,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing GraphR: Accelerating graph processing using ReRAM,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.748141Z

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Observation 5cd336b7-147d-45ae-a722-e46f62fd6d74 · outbound

This paper cites GraphP: Reducing communication for pim-based graph processing with efﬁcient data partition,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing GraphP: Reducing communication for pim-based graph processing with efﬁcient data partition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.729648Z

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Observation 766e6a4c-e103-4ff3-ade0-65b09e56aeb7 · outbound

This paper cites Graphi- cionado: A high-performance and energy-efﬁcient accelerator for graph analytics,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Graphi- cionado: A high-performance and energy-efﬁcient accelerator for graph analytics,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.711016Z

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Observation a011f1ea-382d-4422-a84c-2e4358a7adba · outbound

This paper cites Energy efﬁcient architecture for graph analytics acceler- ators,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Energy efﬁcient architecture for graph analytics acceler- ators,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.693149Z

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Observation 574dcec2-a02d-46c1-b0f2-dd0ab0bfdc65 · outbound

This paper cites Efﬁcient sparse matrix-vector multipli- cation on GPUs using the CSR storage format,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Efﬁcient sparse matrix-vector multipli- cation on GPUs using the CSR storage format,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.663158Z

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Observation 2a4b2880-8fbc-436c-80d5-61b9fdedd7e0 · outbound

This paper cites An efﬁcient GPU general sparse matrix-matrix multiplication for irregular data,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing An efﬁcient GPU general sparse matrix-matrix multiplication for irregular data,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.622093Z

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Observation a6bfe3fb-3b18-4234-83d3-3415345dab71 · outbound

This paper cites Fast sparse matrix-vector multiplication on GPUs for graph appli- cations,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Fast sparse matrix-vector multiplication on GPUs for graph appli- cations,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.589424Z

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Observation 13d6510d-79ef-4556-a658-6e178b597bc0 · outbound

This paper cites Efﬁcient sparse matrix-vector multiplication on CUDA,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Efﬁcient sparse matrix-vector multiplication on CUDA,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.565626Z

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Observation e4135107-6e88-498f-80e8-93bf733b3aff · outbound

This paper cites Implementing sparse matrix-vector mul- tiplication on throughput-oriented processors,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Implementing sparse matrix-vector mul- tiplication on throughput-oriented processors,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.547592Z

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Observation 386e9c58-ea02-439c-9aa9-e2b221abbbf3 · outbound

This paper cites Inductive representation learning on large graphs,.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Inductive representation learning on large graphs,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:31:46.526145Z

Source-reported events for the cited work

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Observation 9f4ff55d-80a5-43cb-aa67-65335625f3e9 · outbound

This paper cites How Powerful are Graph Neural Networks?.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing How Powerful are Graph Neural Networks?

Reference 67

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Source-reported events for the cited work

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Observation 810303b6-1190-42d3-b216-273be7f1787e · outbound

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AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Unresolved cited work

Reference 2018

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Observation 01052b58-1e7c-4b6f-a799-75f21d036e05 · outbound

This paper cites an unresolved cited work.

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing Unresolved cited work

Reference 2019

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