Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training-Reference-Cited by-同舟云学术

Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training

Published:2023 Issue: Volume: Page:515-526
ISSN:0302-9743
Container-title:Image Analysis and Processing – ICIAP 2023
language:
Short-container-title:

Author:

Lazzaro Dario^ORCID,Cinà Antonio Emanuele^ORCID,Pintor Maura^ORCID,Demontis Ambra^ORCID,Biggio Battista^ORCID,Roli Fabio^ORCID,Pelillo Marcello^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43153-1_43

Reference30 articles.

1. Albericio, J., Judd, P., Hetherington, T.H., Aamodt, T.M., Jerger, N.D.E., Moshovos, A.: Cnvlutin: ineffectual-neuron-free deep neural network computing. In: 43rd ACM/IEEE ISCA (2016)

2. Chen, Y., Emer, J.S., Sze, V.: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. In: 43rd ACM/IEEE ISCA (2016)

3. Cinà, A.E., Grosse, K., Demontis, A., Biggio, B., Roli, F., Pelillo, M.: Machine learning security against data poisoning: are we there yet? CoRR (2022)

4. Cinà, A.E., et al.: Wild patterns reloaded: a survey of machine learning security against training data poisoning. ACM Comput. Surv. (2023)

5. Cinà, A.E., Vascon, S., Demontis, A., Biggio, B., Roli, F., Pelillo, M.: The hammer and the nut: is bilevel optimization really needed to poison linear classifiers? In: IJCNN (2021)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Greenlight: Highlighting TensorFlow APIs Energy Footprint;Proceedings of the 21st International Conference on Mining Software Repositories;2024-04-15

2. An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications;Applied Sciences;2024-03-29