Green AI‐Driven Concept for the Development of Cost‐Effective and Energy‐Efficient Deep Learning Method: Application in the Detection of Eimeria Parasites as a Case Study
Acmali, Suheda Semih and Ortakci, Yasin and Şeker, Hüseyin (2024) Green AI‐Driven Concept for the Development of Cost‐Effective and Energy‐Efficient Deep Learning Method: Application in the Detection of Eimeria Parasites as a Case Study. Advanced Intelligent Systems. ISSN 2640-4567
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Advanced Intelligent Systems - 2024 - Acmali - Green AI‐Driven Concept for the Development of Cost‐Effective and.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) |
Abstract
Although large-scale pretrained convolutinal neural networks (CNN) models have shown impressive transfer learning capabilities, they come with drawbacks such as high energy consumption and computational cost due to their potential redundant parameters. This study presents an innovative weight-level pruning technique that mitigates the challenges of overparameterization, and subsequently minimizes the electricity usage of such large deep learning models. The method focuses on removing redundant parameters while upholding model accuracy. This methodology is applied to classify Eimeria species parasites from fowls and rabbits. By leveraging a set of 27 pretrained CNN models with a number of parameters between 3.0M and 118.5M, the framework has identified a 4.8M-parameter model with the highest accuracy for both animals. The model is then subjected to a systematic pruning process, resulting in an 8% reduction in parameters and a 421M reduction in floating point operations while maintaining the same classification accuracy for both fowls and rabbits. Furthermore, unlike the existing literature where two separate models are created for rabbits and fowls, this article presents a combined model with 17 classes. This approach has resulted in a CNN model with nearly 50% reduced parameter size while retaining the same accuracy of over 90%.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.1002/aisy.202300644 | ||||||
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| Subjects: | CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science |
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| Divisions: | Faculty of Computing, Engineering and the Built Environment > College of Computing | ||||||
| Depositing User: | Gemma Tonks | ||||||
| Date Deposited: | 25 Jun 2024 13:17 | ||||||
| Last Modified: | 25 Jun 2024 13:17 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/15589 |
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