Class-specific early exit design methodology for convolutional neural networks (2021)
Bonato, Vanderlei 
; Bouganis, Christos-Savvas
Fonte: Applied Soft Computing Journal. Unidade: ICMC
Assuntos: HARDWARE, INFERÊNCIA, CONSUMO DE ENERGIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BONATO, Vanderlei e BOUGANIS, Christos-Savvas. Class-specific early exit design methodology for convolutional neural networks. Applied Soft Computing Journal, v. 107, p. 1-12, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2021.107316. Acesso em: 07 nov. 2025.APA
Bonato, V., & Bouganis, C. -S. (2021). Class-specific early exit design methodology for convolutional neural networks. Applied Soft Computing Journal, 107, 1-12. doi:10.1016/j.asoc.2021.107316NLM
Bonato V, Bouganis C-S. Class-specific early exit design methodology for convolutional neural networks [Internet]. Applied Soft Computing Journal. 2021 ; 107 1-12.[citado 2025 nov. 07 ] Available from: https://doi.org/10.1016/j.asoc.2021.107316Vancouver
Bonato V, Bouganis C-S. Class-specific early exit design methodology for convolutional neural networks [Internet]. Applied Soft Computing Journal. 2021 ; 107 1-12.[citado 2025 nov. 07 ] Available from: https://doi.org/10.1016/j.asoc.2021.107316
