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Pruning Ratio Optimization with Layer-Wise Pruning Method for Accelerating Convolutional Neural Networks

Koji KAMMA, Sarimu INOUE, Toshikazu WADA

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Summary :

Pruning is an effective technique to reduce computational complexity of Convolutional Neural Networks (CNNs) by removing redundant neurons (or weights). There are two types of pruning methods: holistic pruning and layer-wise pruning. The former selects the least important neuron from the entire model and prunes it. The latter conducts pruning layer by layer. Recently, it has turned out that some layer-wise methods are effective for reducing computational complexity of pruned models while preserving their accuracy. The difficulty of layer-wise pruning is how to adjust pruning ratio (the ratio of neurons to be pruned) in each layer. Because CNNs typically have lots of layers composed of lots of neurons, it is inefficient to tune pruning ratios by human hands. In this paper, we present Pruning Ratio Optimizer (PRO), a method that can be combined with layer-wise pruning methods for optimizing pruning ratios. The idea of PRO is to adjust pruning ratios based on how much pruning in each layer has an impact on the outputs in the final layer. In the experiments, we could verify the effectiveness of PRO.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.1 pp.161-169
Publication Date
2022/01/01
Publicized
2021/09/29
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDP7096
Type of Manuscript
PAPER
Category
Biocybernetics, Neurocomputing

Authors

Koji KAMMA
  Wakayama University
Sarimu INOUE
  Wakayama University
Toshikazu WADA
  Wakayama University

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