3.5k citations · 5.9k across the 14 of their papers we have counts for
4 papers · 1 filter
Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao +1
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with lim…
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Song Han, Junlong Kang, Huizi Mao +9
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models.…
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang +9
Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural netw…
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han, Xingyu Liu, Huizi Mao +4
State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded…