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A low-cost and high-speed hardware implementation of spiking neural network

Publication ,  Journal Article
Zhang, G; Li, B; Wu, J; Wang, R; Lan, Y; Sun, L; Lei, S; Li, H; Chen, Y
Published in: Neurocomputing
March 21, 2020

Spiking neural network (SNN) is a neuromorphic system based on the information process and store procedure of biological neurons. In this paper, a low-cost and high-speed implementation for a spiking neural network based on FPGA is proposed. The LIF (Leaky-Integrate–Fire) neuron model and tempotron supervised learning rules are used to construct the SNN which can be applied to the classification of pictures. A combined circuit instead of lookup table implementation method is proposed to realize the complex computing of kernel function in LIF neuron model. In addition, this work replaces the multiplication operations in the weights training with the arithmetic shift, which can speed up the training efficiency and reduce the consumption of computing resources. Experimental results based on Vertix-7 FPGA shows that the classification accuracy is approximately 96% and the average time for classifying a sample is 0.576us at the maximum frequency 178 MHz which achieves approximately 908,578 speedup compared with the software implementation on Matlab.

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Published In

Neurocomputing

DOI

EISSN

1872-8286

ISSN

0925-2312

Publication Date

March 21, 2020

Volume

382

Start / End Page

106 / 115

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 52 Psychology
  • 46 Information and computing sciences
  • 40 Engineering
  • 17 Psychology and Cognitive Sciences
  • 09 Engineering
  • 08 Information and Computing Sciences
 

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Zhang, G., Li, B., Wu, J., Wang, R., Lan, Y., Sun, L., … Chen, Y. (2020). A low-cost and high-speed hardware implementation of spiking neural network. Neurocomputing, 382, 106–115. https://doi.org/10.1016/j.neucom.2019.11.045
Zhang, G., B. Li, J. Wu, R. Wang, Y. Lan, L. Sun, S. Lei, H. Li, and Y. Chen. “A low-cost and high-speed hardware implementation of spiking neural network.” Neurocomputing 382 (March 21, 2020): 106–15. https://doi.org/10.1016/j.neucom.2019.11.045.
Zhang G, Li B, Wu J, Wang R, Lan Y, Sun L, et al. A low-cost and high-speed hardware implementation of spiking neural network. Neurocomputing. 2020 Mar 21;382:106–15.
Zhang, G., et al. “A low-cost and high-speed hardware implementation of spiking neural network.” Neurocomputing, vol. 382, Mar. 2020, pp. 106–15. Scopus, doi:10.1016/j.neucom.2019.11.045.
Zhang G, Li B, Wu J, Wang R, Lan Y, Sun L, Lei S, Li H, Chen Y. A low-cost and high-speed hardware implementation of spiking neural network. Neurocomputing. 2020 Mar 21;382:106–115.
Journal cover image

Published In

Neurocomputing

DOI

EISSN

1872-8286

ISSN

0925-2312

Publication Date

March 21, 2020

Volume

382

Start / End Page

106 / 115

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 52 Psychology
  • 46 Information and computing sciences
  • 40 Engineering
  • 17 Psychology and Cognitive Sciences
  • 09 Engineering
  • 08 Information and Computing Sciences