分析声事件检测模型训练与推理的能耗，揭示计算量与功耗的关系。
From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems
- 通过实测不同规模模型的能耗，关联计算量与功耗
- 发现参数量和浮点运算量显著影响能源消耗
- 适合关注模型能效的开发者和研究人员
机器学习模型尤其是神经网络的大规模应用引发了对其环境影响的严重关切。近年来，训练和部署这些系统相关的计算成本急剧上升。因此，理解其能源需求至关重要，以便更全面地评估模型，而不仅仅关注性能。本文研究了声事件检测系统中的几种关键神经网络架构，以音频标注任务为例，测量了从小到大不同规模模型在训练和测试阶段的能耗，并建立了能耗、浮点运算次数、参数数量以及GPU/内存利用率之间的复杂关系。
原文摘要 · Abstract (English)
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.
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