211 citations · 301 across the 12 of their papers we have counts for
6 papers · 1 filter
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Sheng Lin, Wei Jiang, Wei Wang +4
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.…
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Wei Niu, Xiaolong Ma, Sheng Lin +5
With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. H…
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Xiaolong Ma, Sheng Lin, Shaokai Ye +10
Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or S…
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Sheng Lin, Xiaolong Ma, Shaokai Ye +3
Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method. A recent work using systematic framework of DNN weight quantiza…
ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
Xiaolong Ma, Geng Yuan, Sheng Lin +3
The state-of-art DNN structures involve high computation and great demand for memory storage which pose intensive challenge on DNN framework resources. To mitigate the challenges,…
FFT-Based Deep Learning Deployment in Embedded Systems
Sheng Lin, Ning Liu, Mahdi Nazemi +4
Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs)…