Avaliação do Consumo de Energia para o Treinamento de Aprendizado de Máquina utilizando Single-board computers baseadas em ARM

  • Felipe Bernardo LNCC
  • André Yokoyama LNCC
  • Bruno Schulze LNCC
  • Mariza Ferro LNCC

Resumo


Neste trabalho é avaliado o uso de placas single-board computers baseadas em ARM para o treinamento de algoritmos de Aprendizado de Máquina (AM). Foi desenvolvido um conjunto experimental treinando o algoritmo XGBoost com 36 configurações de hiperparâmetros em quatro arquiteturas diferentes. Além disso, foi comparado a sua eficiência (consumo energético, custo de aquisição e tempo de execução) com as principais arquiteturas usadas no treinamento de algoritmos de AM (x86 e GPU). Os resultados mostram que este tipo de arquitetura pode se tornar uma alternativa viável e mais verde, não apenas para a inferência, mas também para a fase de treinamento desses algoritmos.

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Publicado
26/10/2021
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BERNARDO, Felipe; YOKOYAMA, André; SCHULZE, Bruno; FERRO, Mariza. Avaliação do Consumo de Energia para o Treinamento de Aprendizado de Máquina utilizando Single-board computers baseadas em ARM. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (WSCAD), 22. , 2021, Belo Horizonte. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 60-71. DOI: https://doi.org/10.5753/wscad.2021.18512.