Accelerating the Run-Time of Convolutional Neural Networks through Weight Pruning and Quantization

Author:

Alhimdiat Rajai1,Ashour Wesam1,Battrawy Ramy2,Stricker Didier2

Affiliation:

1. Islamic University of Gaza, IUG,Computer Engineering dept.,Gaza,Palestine

2. German Research Centre for Artificial Intelligence. DFKI,Kaiserslautern,Germany

Funder

Ministry of Education

CODE

Publisher

IEEE

Reference33 articles.

1. Pruning filters for efficient convnets;li;in 5th International Conference on Learning Representations ICLR 2017 - Conference Track Proceedings,2017

2. Dynamic Network Surgery for Efficient DNNs;guo;Adv Neural Inf Process Syst,2016

3. A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

4. DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

5. Speeding up Convolutional Neural Networks with Low Rank Expansions

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