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Conference Papers Year : 2023

Monitoring environmental impact of DCASE systems: Why and how ?

Abstract

With the increasingly complex models used in machine learning and the large amount of data needed to train these models, machine learning based solutions can have a large environmental impact. Even if a few hundred experiments are sometimes needed to train a working model, the cost of the training phase represents only 10% to 20% of the total CO2 emissions of the related machine learning usage (the rest lying in the inference phase). Yet, as machine listening researchers the largest part of our energy consumption lays in the training phase. Even though models used in machine listening are smaller than those used in natural language processing or image generation, they still present similar problems. Comparing the energy consumption of system trained on different site can be a complex task and the relation between the system performance and its energy footprint can be uneasy to interpret. The aim of this tutorial is to present an overview of existing tools that can be used to monitor energy consumption and computational costs of neural network based models.
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Dates and versions

hal-04286654 , version 1 (15-11-2023)

Identifiers

  • HAL Id : hal-04286654 , version 1

Cite

Constance Douwes, Francesca Ronchini, Romain Serizel. Monitoring environmental impact of DCASE systems: Why and how ?. Detection and Classification of Acoustic Scene and Events (DCASE) Workshop, Sep 2023, Tampere (Finlande), Finland. ⟨hal-04286654⟩
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