12 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Jiandong Mu, Mengdi Wang, Feiwen Zhu +3
Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for appli…
cs.DC2020★ 12 cited
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
Zhen Zheng, Pengzhan Zhao, Guoping Long +6
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range…
cs.NE2018★ 1 cited
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Feiwen Zhu, Jeff Pool, Michael Andersch +2
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network. Following r…