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A research of convolutional neural network model deployment in low- to medium-performance microcontrollers

Published:19 April 2023Publication History

ABSTRACT

Artificial intelligence internet of things (AIoT) is a technology that came into being under the development of artificial intelligence (AI) and Internet of things (IOT) where deep learning is vigorously promoted and used. Compared with the traditional concept of the Internet of things, the main difference of AIoT technology is that it applies interconnected devices which are embedded with the capacity of neural network model reasoning to the perception layer, this reduce reliance on edge servers (especially for neural network model training or reasoning). Thus, the edge devices of the system will get a more intelligent execution power. For the IOT system structures that have been built at present, most of the interconnection devices in the sensing layer, such as data acquisition nodes or execution nodes, are designed with the low and medium performance microcontroller unit as the processing core. After using the technology such like lightweight neural network and global average pooling, we succeed in deploying the convolutional neural network model to the low and medium performance microcontroller. Thus, the original node can get the reasoning result of neural network model in offline state and use it as a decision element for the operation of the system whit a simple modification of the program.

References

  1. Y. Pang, M. Sun, X. Jiang and X. Li, "Convolution in Convolution for Network in Network," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1587-1597, May 2018, doi: 10.1109/TNNLS.2017.2676130.Google ScholarGoogle ScholarCross RefCross Ref
  2. Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size, arXiv:1602.07360v4.Google ScholarGoogle Scholar
  3. Zhu Xuechen, Chen Sanlin, Cai Gang, Huang Zhihong. Convolutional Neural Network Model Compression Method for Reducing Parameter Scale [J]. Computer and Modernization, 2021(09):83-89.Google ScholarGoogle Scholar
  4. Jia Heming, Lang Chunbo, Jiang Zichao. Plant Leaf Disease Identification Method Based on Lightweight Convolutional Neural Network [J], Computer Application. 2021,41(06):1812-1819.Google ScholarGoogle Scholar
  5. Item Xinjian, Song Xiaomin, Zheng Yongping, Wang Haibo, Fang Zhengyang. Research on MobileNet-YOLO-based Embedded Face Detection [J], Journal of China Agricultural Machinery. 2022,43(04):124-130.Google ScholarGoogle Scholar
  6. T.-Y. Hsiao, Y. -C. Chang and C. -T. Chiu, "Filter-based Deep-Compression with Global Average Pooling for Convolutional Networks," 2018 IEEE International Workshop on Signal Processing Systems (SiPS), 2018, pp. 247-251, doi: 10.1109/SiPS.2018.8598453.Google ScholarGoogle ScholarCross RefCross Ref

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          icWCSN '23: Proceedings of the 2023 10th International Conference on Wireless Communication and Sensor Networks
          January 2023
          162 pages
          ISBN:9781450398466
          DOI:10.1145/3585967

          Copyright © 2023 ACM

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          Publication History

          • Published: 19 April 2023

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