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pro vyhledávání: '"Nie, Chengyi"'
The demand for large language model (LLM) inference is gradually dominating the artificial intelligence workloads. Therefore, there is an urgent need for cost-efficient inference serving. Existing work focuses on single-worker optimization and lacks
Externí odkaz:
http://arxiv.org/abs/2405.06856
Adjusting batch sizes and adaptively tuning other hyperparameters can significantly speed up deep neural network (DNN) training. Despite the ubiquity of heterogeneous clusters, existing adaptive DNN training techniques solely consider homogeneous env
Externí odkaz:
http://arxiv.org/abs/2402.05302
Autor:
Rohloff, Alec, Allen, Zackary, Lin, Kung-Min, Okrend, Joshua, Nie, Chengyi, Liu, Yu-Chia, Tseng, Hung-Wei
Advancements in heterogeneous computing technologies enable the significant potential of virtual reality (VR) applications. To offer the best user experience (UX), a system should adopt an untethered, wireless-network-based architecture to transfer V
Externí odkaz:
http://arxiv.org/abs/2101.07327