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:深层学习模型的评估历来侧重于精确度、F1分和相关计量等标准,高计算电量环境的日益普及使得能够创建更深和更复杂的模型,然而,培训此类模型所需的计算需要大量的碳足迹,在这项工作中,我们研究DL模型结构之间的关系及其在能源消耗和在培训过程中产生的二氧化碳排放量方面对环境的影响,通过利用深革命神经网络进行实证研究,具体地说,我们研究:(一) 建筑的影响以及计算所产生能源消耗和排放量的地点;(二) 精确度与能源效率之间的权衡;(三) 使用软件和硬件工具计量能源消耗的方法的差异。