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Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

Author

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  • Valueva, M.V.
  • Nagornov, N.N.
  • Lyakhov, P.A.
  • Valuev, G.V.
  • Chervyakov, N.I.

Abstract

Convolutional neural networks are a promising tool for solving the problem of pattern recognition. Most well-known convolutional neural networks implementations require a significant amount of memory to store weights in the process of learning and working. We propose a convolutional neural network architecture in which the neural network is divided into hardware and software parts to increase performance and reduce the cost of implementation resources. We also propose to use the residue number system (RNS) in the hardware part to implement the convolutional layer of the neural network. Software simulations using Matlab 2018b showed that convolutional neural network with a minimum number of layers can be quickly and successfully trained. The hardware implementation of the convolution layer shows that the use of RNS allows to reduce the hardware costs on 7.86%–37.78% compared to the two’s complement implementation. The use of the proposed heterogeneous implementation reduces the average time of image recognition by 41.17%.

Suggested Citation

  • Valueva, M.V. & Nagornov, N.N. & Lyakhov, P.A. & Valuev, G.V. & Chervyakov, N.I., 2020. "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 232-243.
    • Handle: RePEc:eee:matcom:v:177:y:2020:i:c:p:232-243
    DOI: 10.1016/j.matcom.2020.04.031
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    Cited by:

    1. Xiaochen Ju & Xinxin Zhao & Shengsheng Qian, 2022. "TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection," Mathematics, MDPI, vol. 10(13), pages 1-18, July.
    2. Bahare Andayeshgar & Fardin Abdali-Mohammadi & Majid Sepahvand & Alireza Daneshkhah & Afshin Almasi & Nader Salari, 2022. "Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    3. Jaiyeop Lee & Ilho Kim, 2022. "Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2117-2130, May.
    4. Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D., 2022. "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 451-465.
    5. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    6. Katarzyna Staszak & Bartosz Tylkowski & Maciej Staszak, 2023. "From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring," IJERPH, MDPI, vol. 20(5), pages 1-20, March.
    7. Myeung-Hun Lee & Hyeun-Jun Moon, 2023. "Nonintrusive Load Monitoring Using Recurrent Neural Networks with Occupants Location Information in Residential Buildings," Energies, MDPI, vol. 16(9), pages 1-22, April.

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