Investigating hardware and software aspects in the energy consumption of machine learning: A green AI‐centric analysis

Author:

Yokoyama André M.1,Ferro Mariza2ORCID,de Paula Felipe B.1,Vieira Vitor G.1,Schulze Bruno1

Affiliation:

1. National Laboratory for Scientific Computing (LNCC) Petropólis Rio de Janeiro Brazil

2. Federal Fluminense University (UFF) Niterói Rio de Janeiro Brazil

Abstract

SummaryMuch has been discussed about artificial intelligence's negative environmental impacts due to its power‐hungry Machine Learning algorithms and emissions linked to this. This work discusses three direct impacts of AI on energy consumption associated with computation: the software, the hardware, and the energy source's carbon intensity. We present an up‐to‐date revision of the literature and assess it through experiments. For hardware, we evaluate the use of ARM‐based single‐board computers for training Machine Learning algorithms. An experimental setup was developed training the algorithm XGBoost and its cost‐effectiveness (energy consumption, acquisition cost, and execution time) compared with the X86‐64 and GPU architectures and other algorithms. In addition, the is estimated for these experiments and compared for three energy sources. The results show that this type of architecture can become a viable and greener alternative, not only for inference but also for training these algorithms. Finally, we evaluated low precision for training Random Forest algorithms with different datasets for the software aspect. Results show that is possible energy reduction with no decrease in accuracy.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

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