Proposes dropout-based BayesCVNNs with automated configuration search and FPGA accelerators that deliver 4.5x–13x speedups over GPUs while enabling uncertainty estimation for complex-valued neural networks.
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Pith papers citing it
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cs.AR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Split CNN Inference partitions layers between DPU and GPU with a GNN predictor, reporting up to 3.37x latency reduction over single-device runs and 96.27% GNN accuracy on tested models.
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Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Proposes dropout-based BayesCVNNs with automated configuration search and FPGA accelerators that deliver 4.5x–13x speedups over GPUs while enabling uncertainty estimation for complex-valued neural networks.
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DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference
Split CNN Inference partitions layers between DPU and GPU with a GNN predictor, reporting up to 3.37x latency reduction over single-device runs and 96.27% GNN accuracy on tested models.