A hierarchical search-space planning framework for GPU kernel optimization that delivers stronger overall implementation validity, sample efficiency, and optimization performance than existing training-free methods, while remaining competitive with the training-based CUDA-L1 without additional model training is proposed.
Jing-Hao Wang, Qiqi Gu, Chenpeng Wu et al.· 0 citations
Evaluated on synthetic and large-scale real-world MILP problems, DynSep speeds up average solving time by 64% on easy and medium datasets, and reduces primal-dual gap integral within the given time limit by 16% on hard datasets.
Mingxuan Ye, Jie Wang, Fangzhou Zhu et al.· Neural Information Processin...· 0 citations
FPGAgent is the first task-specification-to-executable HLS generation framework experimentally validated on a well-established benchmark, and the value of end-to-end validation is demonstrated.
Tian-Yun Wang, Wenjie Wang, Jian-Guo Yao et al.· 0 citations
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