Research and analysis of pruning algorithm

Xin Jin; Xuanmin Ma; Li Tao

doi:10.1117/12.2636499

6 May 2022 Research and analysis of pruning algorithm

Xin Jin, Xuanmin Ma, Li Tao

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760Q (2022) https://doi.org/10.1117/12.2636499
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

In recent years, thanks to the development of GPU (Graphic Processing Unit) computing power, deep neural network (DNN) has drawn extensive attention from academia and industry and has been widely used in various fields of artificial intelligence. However, the mainstream deep learning models have some problems such as large storage space consumption and high computational complexity, which makes it difficult for the deep neural network model to be deployed to mobile devices with limited computing resources or applications with strict delay requirements. Therefore, on the premise of minimizing the loss of model accuracy, the algorithm researches of deep neural network compression and acceleration which can lighten the model has been carried out. In recent years, model compression methods such as pruning, quantification and knowledge distillation have emerged. This paper focuses on neural network pruning algorithm. Firstly, this paper expounds the basic methods and classification of neural network pruning algorithms. Then according to the classification of pruning granularity, a more detailed classification, analysis and comparison of specific models are carried out from the algorithm level. Then based on cifar-10 data set, validity tests of some neural network pruning algorithms and contrastive analyses of the experimental data from different models such as the parameters are carried out. Finally, in combination of the recent results of neural network pruning algorithm, we put forward research dilemmas and problems to be solved. Then the future research trends and methods of neural network pruning algorithm are prospected.

Citation Download Citation

Xin Jin, Xuanmin Ma, and Li Tao "Research and analysis of pruning algorithm", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760Q (6 May 2022); https://doi.org/10.1117/12.2636499

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
17 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neural networks

Evolutionary algorithms

Convolution

Convolutional neural networks

Digital filtering

Machine vision

Analytical research

Show All Keywords

Keywords/Phrases

Search In:

Publication Years