The massive use of machine learning models, particularly neural networks, has raised serious concerns about their environmental impact. Indeed, over the last few years we have seen an explosion in the computing costs associated with training and deploying these systems. It is, therefore, crucial to understand their energy requirements in order to better integrate them into the evaluation of models, which has so far focused mainly on performance. In this paper, we study several neural network architectures that are key components of sound event detection systems, using an audio tagging task as an example. We measure the energy consumption for training and testing small to large architectures and establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.

翻译：机器学习模型，尤其是神经网络的大规模应用，已引发对其环境影响的严重关切。事实上，过去几年中，我们见证了与训练和部署这些系统相关的计算成本呈爆炸式增长。因此，理解其能量需求至关重要，以便将其更好地纳入模型评估体系——迄今为止该体系主要聚焦于性能指标。本文以音频标注任务为例，研究了作为声音事件检测系统关键组件的多种神经网络架构。我们测量了从小型到大型架构在训练与测试过程中的能量消耗，并建立了能量消耗、浮点运算次数、参数量以及GPU/内存利用率之间的复杂关系。