The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$_2$ emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.

翻译：深度学习模型的评估传统上侧重于准确率、F1分数及相关指标。高计算能力环境的日益普及使得创建更深层、更复杂的模型成为可能。然而，训练这些模型所需的计算会产生大量碳足迹。本文通过使用深度卷积神经网络的实证研究，探讨了深度学习模型架构与其在训练过程中消耗的能量和产生的二氧化碳排放对环境的影响之间的关系。具体而言，我们研究了：(i) 计算所在架构和位置对能耗和排放的影响；(ii) 准确率与能量效率之间的权衡；以及 (iii) 使用基于软件和基于硬件的工具测量能耗方法的差异。