Energy Efficiency of Training Neural Network Architectures: An Empirical Study

V. R. B. G. Caldiera and H. D. Rombach, “The goal question metric approach,” Encyclopedia of software engineering

1994

Earlier work this paper cites.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE

1998

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems

2012

Earlier work this paper cites.

L. Deng, “The mnist database of handwritten digit images for machine learning research,” IEEE Signal Processing Magazine

2012

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556

Original

2014

Earlier work this paper cites.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al

2015

Earlier work this paper cites.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2016

Earlier work this paper cites.

P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, “Pruning convolutional neural networks for resource efficient inference,” arXiv preprint arXiv:1611.06440

Original

2016

Cited alongside, same era.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2017

Cited alongside, same era.

D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al

Original

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems

2017

Cited alongside, same era.

E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for deep learning in nlp,” arXiv preprint arXiv:1906.02243

Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision

2021

Later among the works it cites.

D. Patterson, J. Gonzalez, Q. Le, C. Liang, L.-M. Munguia, D. Rothchild, D. So, M. Texier, and J. Dean, “Carbon emissions and large neural network training,” arXiv preprint arXiv:2104.10350

Original

2021

Later among the works it cites.

R. Creus, S. Martínez-Fernández, and X. Franch, “Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?,” arXiv preprint arXiv:2109.15284

Original

2021

Later among the works it cites.

R. Creus, S. Martínez-Fernández, and X. Franch, “Integration of convolutional neural networks in mobile applications,” in WAIN@ICSE

2021

Later among the works it cites.