Position: Tensor Networks are a Valuable Asset for Green AI

Eva Memmel, Clara Menzen, Jetze Schuurmans, Frederiek Wesel, Kim Batselier
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:35340-35353, 2024.

Abstract

For the first time, this position paper introduces a fundamental link between tensor networks (TNs) and Green AI, highlighting their synergistic potential to enhance both the inclusivity and sustainability of AI research. We argue that TNs are valuable for Green AI due to their strong mathematical backbone and inherent logarithmic compression potential. We undertake a comprehensive review of the ongoing discussions on Green AI, emphasizing the importance of sustainability and inclusivity in AI research to demonstrate the significance of establishing the link between Green AI and TNs. To support our position, we first provide a comprehensive overview of efficiency metrics proposed in Green AI literature and then evaluate examples of TNs in the fields of kernel machines and deep learning using the proposed efficiency metrics. This position paper aims to incentivize meaningful, constructive discussions by bridging fundamental principles of Green AI and TNs. We advocate for researchers to seriously evaluate the integration of TNs into their research projects, and in alignment with the link established in this paper, we support prior calls encouraging researchers to treat Green AI principles as a research priority.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-memmel24a, title = {Position: Tensor Networks are a Valuable Asset for Green {AI}}, author = {Memmel, Eva and Menzen, Clara and Schuurmans, Jetze and Wesel, Frederiek and Batselier, Kim}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {35340--35353}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/memmel24a/memmel24a.pdf}, url = {https://proceedings.mlr.press/v235/memmel24a.html}, abstract = {For the first time, this position paper introduces a fundamental link between tensor networks (TNs) and Green AI, highlighting their synergistic potential to enhance both the inclusivity and sustainability of AI research. We argue that TNs are valuable for Green AI due to their strong mathematical backbone and inherent logarithmic compression potential. We undertake a comprehensive review of the ongoing discussions on Green AI, emphasizing the importance of sustainability and inclusivity in AI research to demonstrate the significance of establishing the link between Green AI and TNs. To support our position, we first provide a comprehensive overview of efficiency metrics proposed in Green AI literature and then evaluate examples of TNs in the fields of kernel machines and deep learning using the proposed efficiency metrics. This position paper aims to incentivize meaningful, constructive discussions by bridging fundamental principles of Green AI and TNs. We advocate for researchers to seriously evaluate the integration of TNs into their research projects, and in alignment with the link established in this paper, we support prior calls encouraging researchers to treat Green AI principles as a research priority.} }
Endnote
%0 Conference Paper %T Position: Tensor Networks are a Valuable Asset for Green AI %A Eva Memmel %A Clara Menzen %A Jetze Schuurmans %A Frederiek Wesel %A Kim Batselier %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-memmel24a %I PMLR %P 35340--35353 %U https://proceedings.mlr.press/v235/memmel24a.html %V 235 %X For the first time, this position paper introduces a fundamental link between tensor networks (TNs) and Green AI, highlighting their synergistic potential to enhance both the inclusivity and sustainability of AI research. We argue that TNs are valuable for Green AI due to their strong mathematical backbone and inherent logarithmic compression potential. We undertake a comprehensive review of the ongoing discussions on Green AI, emphasizing the importance of sustainability and inclusivity in AI research to demonstrate the significance of establishing the link between Green AI and TNs. To support our position, we first provide a comprehensive overview of efficiency metrics proposed in Green AI literature and then evaluate examples of TNs in the fields of kernel machines and deep learning using the proposed efficiency metrics. This position paper aims to incentivize meaningful, constructive discussions by bridging fundamental principles of Green AI and TNs. We advocate for researchers to seriously evaluate the integration of TNs into their research projects, and in alignment with the link established in this paper, we support prior calls encouraging researchers to treat Green AI principles as a research priority.
APA
Memmel, E., Menzen, C., Schuurmans, J., Wesel, F. & Batselier, K.. (2024). Position: Tensor Networks are a Valuable Asset for Green AI. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:35340-35353 Available from https://proceedings.mlr.press/v235/memmel24a.html.

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