Optimizing Convolutional Neural Network Architectures
Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/19/3032 |
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| author | Luis Balderas Miguel Lastra José M. Benítez |
| author_facet | Luis Balderas Miguel Lastra José M. Benítez |
| author_sort | Luis Balderas |
| collection | DOAJ |
| description | Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices. |
| format | Article |
| id | doaj-art-6d69ffb387924d4c90d14034d794d636 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-6d69ffb387924d4c90d14034d794d6362025-08-20T02:16:54ZengMDPI AGMathematics2227-73902024-09-011219303210.3390/math12193032Optimizing Convolutional Neural Network ArchitecturesLuis Balderas0Miguel Lastra1José M. Benítez2Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, SpainDistributed Computational Intelligence and Time Series Lab, University of Granada, 18071 Granada, SpainDepartment of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, SpainConvolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices.https://www.mdpi.com/2227-7390/12/19/3032convolutional neural network simplificationneural network pruningefficient machine learningGreen AI |
| spellingShingle | Luis Balderas Miguel Lastra José M. Benítez Optimizing Convolutional Neural Network Architectures Mathematics convolutional neural network simplification neural network pruning efficient machine learning Green AI |
| title | Optimizing Convolutional Neural Network Architectures |
| title_full | Optimizing Convolutional Neural Network Architectures |
| title_fullStr | Optimizing Convolutional Neural Network Architectures |
| title_full_unstemmed | Optimizing Convolutional Neural Network Architectures |
| title_short | Optimizing Convolutional Neural Network Architectures |
| title_sort | optimizing convolutional neural network architectures |
| topic | convolutional neural network simplification neural network pruning efficient machine learning Green AI |
| url | https://www.mdpi.com/2227-7390/12/19/3032 |
| work_keys_str_mv | AT luisbalderas optimizingconvolutionalneuralnetworkarchitectures AT miguellastra optimizingconvolutionalneuralnetworkarchitectures AT josembenitez optimizingconvolutionalneuralnetworkarchitectures |