Novel Pruning of Dendritic Neuron Models for Improved System Implementation and Performance

Xiaohao Wen, Mengchu Zhou, Xudong Luo, Lukui Huang, Ziyue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pruning is widely used for neural network model compression. It removes redundant links from a weight tensor to lead to smaller and more efficient neural networks for system implementation. A compressed neural network can enable faster run and reduced computational cost in network training. In this paper, a novel pruning method is proposed for a dendritic neuron model (DNM). It calculates the significance of each DNM dendrite. The calculated significance is expressed numerically and a dendrite whose significance is lower than a pre-set threshold is removed. Experimental results verify that it obtains superior performance over the existing one in terms of both accuracy and computational efficiency.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1559-1564
Number of pages6
ISBN (Electronic)9781665442077
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia
Duration: Oct 17 2021Oct 20 2021

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period10/17/2110/20/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Keywords

  • Complex systems
  • Dendritic Neuron Model (DNM)
  • Machine learning
  • Neural network
  • Pruning

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