64 citations · 129 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
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…
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
Songze Li, Mingchao Yu, Chien-Sheng Yang +3
Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current…
On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast
Mingchao Yu, Alex Sprintson, Parastoo Sadeghi
We apply linear network coding (LNC) to broadcast a block of data packets from one sender to a set of receivers via lossy wireless channels, assuming each receiver already possesse…