131 citations · 326 across the 20 of their papers we have counts for
3 papers · 1 filter
Bit-Parallel Vector Composability for Neural Acceleration
Soroush Ghodrati, Hardik Sharma, Cliff Young +2
Conventional neural accelerators rely on isolated self-sufficient functional units that perform an atomic operation while communicating the results through an operand delivery-aggr…
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…