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Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networks

arXiv 2502.17734 ↗First posted 25 Feb 2025cs.LG cs.SEcited by 0

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Abstract (arXiv)

This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-efficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy efficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices.

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References · 20

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8 more
  1. 2020Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselvescited by 58
  2. 2020Can Federated Learning Save The Planet?cited by 52
  3. 2020Towards Accurate and Reliable Energy Measurement of NLP Modelscited by 16
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  5. 2024Sustainable Supercomputing for AI: GPU Power Capping at HPC Scalecited by 9
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  7. 2016A Survey and Measurement Study of GPU DVFS on Energy Conservationcited by 6
  8. 2023FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Searchcited by 6

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