Earticle

초록

영어

Deep neural networks (DNNs) have been widely used in various applications, however, the computational complexity and memory requirements of DNNs are becoming increasingly challenging, especially in resource-constrained devices such as mobile phones and embedded systems. In this paper, we propose a lightweight DNN model using channel pruning to address the computational complexity and memory requirements of DNNs in resource-constrained devices. Our approach combines channel pruning with transfer learning to maintain accuracy. Evaluation on the CIFAR-10 dataset shows improved performance with 78% test accuracy, 89% train accuracy, and 73% validation accuracy compared to the unpruned model. The pruned model is suitable for applications with limited computational resources.

목차

Abstract
1. Introduction
2. Related Works
3. Proposed Methodology
3.1. Proposed Framework
4. Experiments
4.1. Experimental setup
4.2. Dataset
4.3. Experimental result
5. Conclusions
Acknowledgment
References

키워드

저자

• Sara Sualiheen [ Department of Electrical and Computer Engineering Inha University, Incheon, South Korea ]
• Deok-Hwan Kim [ Department of Electrical and Computer Engineering Inha University, Incheon, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004