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Cited by 16Green IT and SustainabilityEnergy Efficiency in ComputingEnergy Efficiency and Management

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

A Unified Framework for Assessing Energy Efficiency of Machine Learning

Fischer, Raphael, Jakobs, Matthias, Mücke, Sascha, Morik, Katharina·2023·Communications in Computer and Information Science
DOI 10.1007/978-3-031-23618-1_3
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Crossref · 10.1007/978-3-031-23618-1_3

Cites

8 of 8 references verified0 citation errors · 0 not checkable

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Citations

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Show all 8 references

Research works printed but not linked: 23

  1. Anthony, L.F.W., Kanding, B., Selvan, R.: Carbontracker: tracking and predicting the carbon footprint of training deep learning models. In: ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems (2020). arXiv:2007.03051 · arXiv
  2. Bannink, T., et al.: Larq compute engine: Design, benchmark, and deploy state-of-the-art binarized neural networks (2020). https://arxiv.org/abs/2011.09398 · arXiv
  3. Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., Bao, M.: The values encoded in machine learning research (2021). https://arxiv.org/abs/2106.15590 · DOI · Paper page
  4. Brundage, M., et al.: Toward trustworthy AI development: mechanisms for supporting verifiable claims (2020). https://arxiv.org/abs/2004.07213 · arXiv
  5. Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. (JAIR) 70, 245–317 (2021). https://doi.org/10.1613/jair.1.12228 · DOI · Paper page
  6. Buschjäger, S., Pfahler, L., Buss, J., Morik, K., Rhode, W.: On-site Gamma-Hadron separation with deep learning on FPGAs. In: Dong, Y., Mladenić, D., Saunders, C. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12460, pp. 478–493. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67667-4_29 · DOI
  7. Chatila, R., et al.: Trustworthy AI, pp. 13–39 (2021). https://doi.org/10.1007/978-3-030-69128-8_2 · DOI
  8. Elsayed, N., Maida, A.S., Bayoumi, M.: A review of quantum computer energy efficiency. In: Green Technologies Conference, pp. 1–3 (2019) · DOI
  9. García-Martín, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75–88 (2019) · DOI
  10. Henderson, P., et al.: Towards the systematic reporting of the energy and carbon footprints of machine learning (2020). https://arxiv.org/abs/2002.05651 · arXiv
  11. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). http://arxiv.org/abs/1704.04861 · arXiv
  12. Huang, X., et al.: A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability. Comput. Sci. Rev. 37, 100270 (2020). https://doi.org/10.1016/j.cosrev.2020.100270 · DOI
  13. Kourfali, A., Stroobandt, D.: In-circuit debugging with dynamic reconfiguration of FPGA interconnects. Trans. Reconfigurable Technol. Syst. 13(1), 1–29 (2020) · DOI
  14. Morik, K., et al.: Yes we care! - certification for machine learning methods through the care label framework (2021). https://arxiv.org/abs/2105.10197 · DOI
  15. Mücke, S., Piatkowski, N., Morik, K.: Hardware acceleration of machine learning beyond linear algebra. In: Cellier, P., Driessens, K. (eds.) ECML PKDD 2019. CCIS, vol. 1167, pp. 342–347. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43823-4_29 · DOI
  16. Patterson, D.A., et al.: Carbon emissions and large neural network training (2021). https://arxiv.org/abs/2104.10350 · arXiv
  17. Rauber, J., Brendel, W., Bethge, M.: Foolbox: A Python toolbox to benchmark the robustness of machine learning models (2017). https://arxiv.org/abs/1707.04131 · arXiv
  18. Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. Commun. ACM 63(12), 54–63 (2020). https://doi.org/10.1145/3381831 · DOI · Paper page
  19. Seifert, C., Scherzinger, S., Wiese, L.: Towards generating consumer labels for machine learning models. In: International Conference on Cognitive Machine Intelligence, pp. 173–179 (2019). https://doi.org/10.1109/CogMI48466.2019.00033 · DOI
  20. Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP (2019). http://arxiv.org/abs/1906.02243 · DOI · Paper page
  21. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th International Conference on Machine Learning, pp. 6105–6114 (2019). https://proceedings.mlr.press/v97/tan19a.html
  22. Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. Newsl. 15(2), 49–60 (2014) · DOI
  23. Zhuang, D., Zhang, X., Song, S.L., Hooker, S.: Randomness in neural network training: characterizing the impact of tooling (2021). https://arxiv.org/abs/2106.11872 · arXiv

Metadata

Authors
Fischer, R., Jakobs, M., Mücke, S., & Morik, K.
Affiliations
TU Dortmund University (DE)
Corresponding author
Fischer, Raphael
ORCID
Venue
Communications in Computer and Information Science
Publisher
Springer Nature Switzerland
Published
2023
Pages
39-54
ISSN
  • 1865-0929 (ISSN-L)
  • 1865-0937
DOI
10.1007/978-3-031-23618-1_3
OpenAlex
W4318465316
Cited by
16
References
8
Citations by year
2026: 4 · 2025: 3 · 2024: 6 · 2023: 3
FWCI
7.23
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Communications in computer and information science (publishedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
Keywords
energy footprint, efficiency indicators, energy labelling, performance-efficiency trade-off

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