Surrogate-based constrained multi-objective optimization for the compression of CNNs

  • Gabriel Bicalho Ferreira Universidade Federal de Ouro Preto
  • Antônio de Barros Universidade Federal de Ouro Preto
  • Issah Ibrahim McGill University
  • Rodrigo Silva Universidade Federal de Ouro Preto

Resumo


A implantação de redes neurais profundas em dispositivos com recursos limitados apresenta desafios significativos. Para enfrentar esse problema, técnicas como poda e quantização são frequentemente utilizadas. No entanto, essas técnicas exigem ajustes cuidadosos para garantir que o modelo final seja computacionalmente eficiente sem sacrificar muita qualidade. Para automatizar o processo de compressão e melhorar sua robustez e acessibilidade, propomos um Algoritmo Evolucionário Multiobjetivo com Restrições Baseado em Surrogates para a compressão de redes neurais artificiais. Resultados experimentais obtidos para o conjunto de dados CIFAR10 utilizando as arquiteturas ResNet50 e VGG16 indicam que a abordagem proposta permite um ajuste mais eficiente em relação aos algoritmos de poda e quantização, resultando em conjuntos não dominados de maior qualidade em comparação com o método de otimização sem Surrogates. Além disso, nosso método produziu versões mais enxutas das arquiteturas testadas mantendo a precisão.

Palavras-chave: Convolutional Neural Networks, Multiobjective Optimization, Neural Network Compression

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Publicado
25/09/2023
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FERREIRA, Gabriel Bicalho; BARROS, Antônio de; IBRAHIM, Issah; SILVA, Rodrigo. Surrogate-based constrained multi-objective optimization for the compression of CNNs. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 184-198. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.233874.