205 citations · 328 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2018★ 205 cited
Training Deep Neural Networks with 8-bit Floating Point Numbers
Naigang Wang, Jungwook Choi, Daniel Brand +2
The state-of-the-art hardware platforms for training Deep Neural Networks (DNNs) are moving from traditional single precision (32-bit) computations towards 16 bits of precision --…
cs.LG2017★ 74 cited
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Chia-Yu Chen, Jungwook Choi, Daniel Brand +3
Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communicatio…
cs.LG2017★ 49 cited
MEC: Memory-efficient Convolution for Deep Neural Network
Minsik Cho, Daniel Brand
Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor…