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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 2

Comparative Analysis of Open-Source Deep Learning Models in Terms of Energy Consumption, Computational Load, and Performance

Yasin Sancar(ATATÜRK ÜNİVERSİTESİ)0000-0002-4200-1293
Pages
616–623
Publication Date
2026-03-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results reveal that model architecture has a direct impact on energy consumption and that model selection plays a critical role in the design of sustainable artificial intelligence systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The energy consumption of deep learning models during training and inference processes has become an important performance indicator, especially for applications running on resource-constrained devices.
📊
Key Findings & Data:Although there are significant differences in computational costs between different architectures, studies that comprehensively compare the energy efficiency of models are limited.
💡
Conclusion & Impact:The results reveal that model architecture has a direct impact on energy consumption and that model selection plays a critical role in the design of sustainable artificial intelligence systems.
🔬Design:Modeling & Simulation
📍Scope:2070 Verileri
⚙️Variables:Energy consumption · Deep learning · Power profiling

Abstract

The energy consumption of deep learning models during training and inference processes has become an important performance indicator, especially for applications running on resource-constrained devices. Although there are significant differences in computational costs between different architectures, studies that comprehensively compare the energy efficiency of models are limited. In this study, six widely used models MobileNetV2, EfficientNet-B0, ResNet50, DenseNet121, Xception, and VGG19 were trained and evaluated under the same dataset and experimental settings. Real-time power measurements were performed on an RTX 2070 GPU to calculate each model's total energy consumption during training and inference, average power value, frames per second (FPS), and energy cost per image (J/image). The findings show that lightweight architectures are significantly more efficient: MobileNetV2 achieved the lowest energy consumption at 0.2289 J/image during inference, while EfficientNet-B0 offered balanced performance in terms of accuracy and energy usage. In contrast, VGG19 stood out as the least efficient model due to its high power requirements. The results reveal that model architecture has a direct impact on energy consumption and that model selection plays a critical role in the design of sustainable artificial intelligence systems.

Özet

The energy consumption of deep learning models during training and inference processes has become an important performance indicator, especially for applications running on resource-constrained devices. Although there are significant differences in computational costs between different architectures, studies that comprehensively compare the energy efficiency of models are limited. In this study, six widely used models MobileNetV2, EfficientNet-B0, ResNet50, DenseNet121, Xception, and VGG19 were trained and evaluated under the same dataset and experimental settings. Real-time power measurements were performed on an RTX 2070 GPU to calculate each model's total energy consumption during training and inference, average power value, frames per second (FPS), and energy cost per image (J/image). The findings show that lightweight architectures are significantly more efficient: MobileNetV2 achieved the lowest energy consumption at 0.2289 J/image during inference, while EfficientNet-B0 offered balanced performance in terms of accuracy and energy usage. In contrast, VGG19 stood out as the least efficient model due to its high power requirements. The results reveal that model architecture has a direct impact on energy consumption and that model selection plays a critical role in the design of sustainable artificial intelligence systems.

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Cite This Article

Sancar, Y. (2026). Comparative Analysis of Open-Source Deep Learning Models in Terms of Energy Consumption, Computational Load, and Performance. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 2, 616–623. https://doi.org/10.34248/bsengineering.1858749

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