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cs.LG2025
MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators
Cheng Wan, Runkai Tao, Zheng Du +2
Graph convolutional networks (GCNs) have demonstrated superiority in graph-based learning tasks. However, training GCNs on full graphs is particularly challenging, due to the follo…
cs.LG2024
MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
Yongan Zhang, Zhongzhi Yu, Yonggan Fu +2
Large Language Models (LLMs) have recently shown promise in streamlining hardware design processes by encapsulating vast amounts of domain-specific data. In addition, they allow us…