131 citations · 326 across the 20 of their papers we have counts for
Showing 2018 · cs.LGShow all
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cs.LG2018★ 10 cited
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra +6
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To allev…
cs.LG2018★ 64 cited
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li, Mingchao Yu, Songze Li +3
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical dis…