Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...
1. T.N. Truong, F. Trahay, J. Domke, A. Drozd, E. Vatai, J. Liao, M. Wahib, B. Gerofi, "Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep ...
The enormous growth in artificial intelligence (AI) and Internet of Things (IoT) is fueling a growing demand for high-efficiency computing to perform real-time analysis on massive amounts of data. In ...
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