System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
861-865,879

Design of depthwise separable neural network models and hardware accelerator for small-scale edge computing

Meng Qunkang1
Li Qiang2
Zhao Feng2
Zhuang Li3
Wang Qiulin3
Chen Kai3
Luo Jun4
Chang Sheng1
1. School of Physics and Technology, Wuhan University, Wuhan 430072, China
2. State Grid Information & Telecommunication Co. , Ltd. , Beijing 102211, China
3. Fujian Yirong Information Technology Co. , Ltd. , Fuzhou 350003, China
4. Institute of Electronic Fifth Research Dept. , Ministry of Industry and Information Technology, Guangzhou 510507, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0335

Abstract

The parameter and computational requirements of neural networks have increased, making it increasingly difficult to deploy neural networks on hardware platforms with limited resources. This paper proposed a method to address the challenge of deploying deep learning models on small edge computing platforms. The method utilized a depthwise separable network model applied to a custom dataset. This method carried out model compression on the software end by employing steps as transfer learning, sensitivity analysis, and pruning quantization. On the hardware end, it analyzed and designed a pipeline hardware accelerator suitable for FPGA with limited resources. Experimental results demonstrate that after software-based network compression optimization, this quantized deployment model achieves a high accuracy rate of 94.60%, with a lower singleinference fixed-point operation count of 16.64 M and a parameter count of 0.079 M. Furthermore, after hardware resource optimization, the pipeline deployment on a domestic FPGA development board achieved an inference frame rate of 366 FPS and a computational efficiency of 8.57 GOPS/W. This research provides a solution for high-performance deployment of deep learning models on small-scale edge computing platforms.

Foundation Support

国家自然科学基金资助项目(62074116,61874079)
广东省基础与应用基础研究基金资助项目(2021A1515110939)
武汉大学珞珈青年学者基金资助项目
电网人工智能模型优化研究项目(SGITYLYRWZXX2202264)
武汉市知识创新专项资助项目(2023010201010077)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0335
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: System Development & Application
Pages: 861-865,879
Serial Number: 1001-3695(2024)03-032-0861-05

Publish History

[2023-10-12] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

孟群康, 李强, 赵峰, 等. 面向小型边缘计算的深度可分离神经网络模型与硬件加速器设计 [J]. 计算机应用研究, 2024, 41 (3): 861-865,879. (Meng Qunkang, Li Qiang, Zhao Feng, et al. Design of depthwise separable neural network models and hardware accelerator for small-scale edge computing [J]. Application Research of Computers, 2024, 41 (3): 861-865,879. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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