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- Song Han, Huizi Mao, William J. Dally
- ICLR
- 2015
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce… (More)
- Song Han, Jeff Pool, John Tran, William J. Dally
- NIPS
- 2015
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts;… (More)
Recent research on deep convolutional neural networks (CNNs) has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple CNN architectures… (More)
Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that… (More)
- Song Han, Xingyu Liu, +4 authors William J. Dally
- ISCA
- 2016
- Jianping Song, Song Han, +4 authors Wally Pratt
- IEEE Real-Time and Embedded Technology and…
- 2008
Wireless technology has been regarded as a paradigm shifter in the process industry. The first open wireless communication standard specifically designed for process measurement and control… (More)
- Song Han, Junlong Kang, +9 authors William J. Dally
- FPGA
- 2016
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built increasingly larger models. Such large model… (More)
- Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally
- ICLR
- 2016
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 limited power budgets.… (More)
- Han Cai, Ligeng Zhu, Song Han
- ICLR
- 2018
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms… (More)
- Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han
- ECCV
- 2018
Model compression is an effective technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model… (More)