Dynamic Sparse Graph for Efficient Deep Learning

no code implementations • ICLR 2019 • Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie

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.

Dimensionality Reduction

Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training

no code implementations • 1 Jun 2018 • Maohua Zhu, Jason Clemons, Jeff Pool, Minsoo Rhu, Stephen W. Keckler, Yuan Xie

Further, we can enforce structured sparsity in the gate gradients to make the LSTM backward pass up to 45% faster than the state-of-the-art dense approach and 168% faster than the state-of-the-art sparsifying method on modern GPUs.

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

no code implementations • 20 Jun 2016 • Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

To improve the performance and maintain the scalability, we present CNNLab, a novel deep learning framework using GPU and FPGA-based accelerators.

Scheduling