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

Document Type

Conference Proceeding

Publication Date

1-1-2021

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.

Identifier

85124305160 (Scopus)

ISBN

[9781665442077]

Publication Title

Conference Proceedings IEEE International Conference on Systems Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC52423.2021.9659103

ISSN

1062922X

First Page

1559

Last Page

1564

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