Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point

Author:

Liu Fangxin1,Zhao Wenbo1,He Zhezhi1,Wang Yanzhi2,Wang Zongwu1,Dai Changzhi3,Liang Xiaoyao1,Jiang Li1

Affiliation:

1. Shanghai Jiao Tong University

2. Northeastern University

3. DeepBlue Technology (Shanghai) Co., Ltd.

Funder

National Natural Science Foundation of China

Publisher

IEEE

Reference35 articles.

1. Lq-nets: Learned quantization for highly accurate and compact deep neural networks;zhang;Proceedings of the European Conference on Computer Vision (ECCV),2018

2. A unified framework of dnn weight pruning and weight clustering/quantization using admm;ye,2018

3. Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm;ye,2019

4. Mixed precision quantization of convnets via differentiable neural architecture search;wu,2018

5. Incremental network quantization: Towards lossless cnns with low-precision weights;zhou;5th International Conference on Learning Representations (ICLR),2017

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