18 citations · 32 across the 4 of their papers we have counts for
4 papers
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
Wei Niu, Zhengang Li, Xiaolong Ma +6
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resou…
CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks
Runbin Shi, Peiyan Dong, Tong Geng +6
Recurrent neural networks (RNNs) have been widely adopted in temporal sequence analysis, where realtime performance is often in demand. However, RNNs suffer from heavy computationa…
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Peiyan Dong, Siyue Wang, Wei Niu +8
Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniqu…
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
Ao Ren, Tao Zhang, Yuhao Wang +5
The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial intelligence applications on resource constrained devices, such as mobile and wearable de…