HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
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Pith papers citing it
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Introduces hGAO and cGAO operators for graph representation learning that outperform standard graph attention operators in accuracy while reducing computational requirements.
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Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
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Graph Representation Learning via Hard and Channel-Wise Attention Networks
Introduces hGAO and cGAO operators for graph representation learning that outperform standard graph attention operators in accuracy while reducing computational requirements.