开发具有成本效益和能效的深度学习方法的绿色人工智能驱动理念:以埃默氏寄生虫检测中的应用为例

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-12 DOI:10.1002/aisy.202300644
Suheda Semih Acmali, Yasin Ortakci, Huseyin Seker
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引用次数: 0

摘要

尽管大规模预训练的卷积神经网络(CNN)模型已显示出令人印象深刻的迁移学习能力,但由于其潜在的冗余参数,它们也存在能耗高、计算成本高等缺点。本研究提出了一种创新的权重级剪枝技术,可减轻参数过多带来的挑战,从而最大限度地降低此类大型深度学习模型的耗电量。该方法侧重于去除冗余参数,同时保持模型的准确性。该方法被应用于对家禽和兔子的艾美耳种寄生虫进行分类。通过利用一组参数数在 3.0M 到 118.5M 之间的 27 个预训练 CNN 模型,该框架确定了一个参数数为 4.8M 的模型,该模型对这两种动物的分类准确率最高。随后,对该模型进行了系统剪枝处理,从而减少了 8% 的参数和 4.21 亿次浮点运算,同时保持了对鸡和兔子的相同分类准确性。此外,与现有文献中为兔子和家禽创建两个独立模型的做法不同,本文提出了一个包含 17 个类别的组合模型。这种方法使 CNN 模型的参数大小减少了近 50%,而准确率却保持在 90% 以上。
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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

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%.

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CiteScore
1.30
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审稿时长
4 weeks
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