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Found 3 papers, 2 papers with code
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
Our methodology involves storing the whole dataset directly in INR format on a GPU, mitigating the significant data communication overhead between the CPU and GPU during training.
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference
The experiment results demonstrate that DGNN-Booster can achieve a speedup of up to 5. 6x compared to the CPU baseline (6226R), 8. 4x compared to the GPU baseline (A6000) and 2. 1x compared to the FPGA baseline without applying optimizations proposed in this paper.
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU
A variety of dynamic graph neural networks designed from algorithmic perspectives have succeeded in incorporating temporal information into graph processing.
