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.

翻译：本文提出Phoeni6，一种在秉持公平比较与可复现性原则下评估神经网络能耗的系统化方法。Phoeni6为管理能耗相关数据与配置提供了一个综合性解决方案，确保评估过程中的可移植性、透明性与协调性。该方法通过容器化工具、稳健的数据库管理以及灵活的数据模型，实现了能耗评估的自动化。在第一个案例研究中，使用原始图像与调整尺寸后的图像对比了AlexNet与MobileNet的能耗。结果表明，在保持相近准确率水平的前提下，MobileNet对于原始图像的能效最高可提升6.25%，对于调整尺寸后的数据集则可提升2.32%。在第二个研究中，评估了图像文件格式对能耗的影响。与PNG格式相比，BMP格式的图像可降低高达30%的能耗，这凸显了文件格式对能效的影响。这些发现强调了Phoeni6在优化多样化神经网络应用能耗以及建立可持续人工智能实践方面的重要性。