GraphLeap decouples per-layer graph construction from feature updates in Vision GNNs by using previous-layer features for the current graph, enabling pipelined FPGA acceleration with up to 95.7× CPU speedup after fine-tuning.
Hygcn: A gcn accelerator with hybrid architecture
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FlexVector achieves 3.78x speedup and 40.5% lower energy for GCN inference on five real-world datasets by using flexible VRFs and graph preprocessing to match varying-sparsity graphs.
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GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA
GraphLeap decouples per-layer graph construction from feature updates in Vision GNNs by using previous-layer features for the current graph, enabling pipelined FPGA acceleration with up to 95.7× CPU speedup after fine-tuning.
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FlexVector: A SpMM Vector Processor with Flexible VRF for GCNs on Varying-Sparsity Graphs
FlexVector achieves 3.78x speedup and 40.5% lower energy for GCN inference on five real-world datasets by using flexible VRFs and graph preprocessing to match varying-sparsity graphs.