177 citations · 559 across the 13 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
cs.NE2018★ 30 cited
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang +8
Weight pruning methods of DNNs have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage require…
cs.DC2018★ 10 cited
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Caiwen Ding, Ao Ren, Geng Yuan +5
Both industry and academia have extensively investigated hardware accelerations. In this work, to address the increasing demands in computational capability and memory requirement,…
cs.LG2018★ 82 cited
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Ruizhe Cai, Ao Ren, Ning Liu +5
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probabi…