18 citations · 29 across the 3 of their papers we have counts for
3 papers
cs.LG2018★ 4 cited
Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu +4
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference…
cs.LG2018★ 7 cited
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool +3
Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate…
cs.LG2016★ 18 cited
CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis
Maohua Zhu, Liu Liu, Chao Wang +1
Designing and implementing efficient, provably correct parallel neural network processing is challenging. Existing high-level parallel abstractions like MapReduce are insufficientl…