From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems
This addresses the environmental impact of machine learning for researchers and practitioners in audio processing, though it is incremental as it applies existing methods to new data.
The paper studied the energy consumption of neural networks in sound event detection systems, finding complex relationships between energy use, computational operations, and hardware utilization during training and testing.
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.