Accelerating Graph Neural Networks in Pytorch with HLS and Deep Dataflows
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42921-7_9
Reference14 articles.
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3. Sarkar, R., Abi-Karam, S., He, Y., Sathidevi, L., Hao, C.: FlowGNN: a dataflow architecture for real-time workload-agnostic graph neural network inference. In: 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA), Los Alamitos, CA, USA, pp. 1099–1112. IEEE Computer Society (2023)
4. Garg, R., et al.: Understanding the design-space of sparse/dense multiphase GNN dataflows on spatial accelerators (2021)
5. Peltekis, C., Filippas, D., Nicopoulos, C., Dimitrakopoulos, G.: FusedGCN: a systolic three-matrix multiplication architecture for graph convolutional networks. In: 2022 IEEE 33rd International Conference on Application-Specific Systems, Architectures and Processors (ASAP), pp. 93–97 (2022)
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