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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2021

15.10.2020 | Original Article

The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

verfasst von: Dou El Kefel Mansouri, Bachir Kaddar, Seif-Eddine Benkabou, Khalid Benabdeslem

Erschienen in: Neural Computing and Applications | Ausgabe 12/2021

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Abstract

In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining hardware and specialization techniques. Since the pooling step represents a process that contributes significantly to CNNs performance improvement, we propose the Mode-Fisher pooling method. This form of pooling can potentially offer a very promising results in terms of improving feature extraction performance. The proposed method reduces significantly the data movement in the CNN and save up to 10% of total energy, without any performance penalty.
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Fußnoten
1
Computer file that contains an uncompressed image. It is not viewable directly by most computer systems.
 
2
In the literature, the most used filters do not exceed the size (\(5 \times 5\)).
 
3
Max pooling: y= Max (\(x_{ij}\)).
 
4
Average pooling: y= Mean (\(x_{ij}\)),y represents the output, i and j are the row and column index of the pooling region.
 
5
All data sets are summarized in Table 1.
 
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Metadaten
Titel
The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks
verfasst von
Dou El Kefel Mansouri
Bachir Kaddar
Seif-Eddine Benkabou
Khalid Benabdeslem
Publikationsdatum
15.10.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05406-4

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