Position: Tensor Networks are a Valuable Asset for Green AI
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866912275175047168 |
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| author | Memmel, Eva Menzen, Clara Schuurmans, Jetze Wesel, Frederiek Batselier, Kim |
| author_facet | Memmel, Eva Menzen, Clara Schuurmans, Jetze Wesel, Frederiek Batselier, Kim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_12961 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Position: Tensor Networks are a Valuable Asset for Green AI Memmel, Eva Menzen, Clara Schuurmans, Jetze Wesel, Frederiek Batselier, Kim Machine Learning Artificial Intelligence I.2.0; K.4.0 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. |
| title | Position: Tensor Networks are a Valuable Asset for Green AI |
| topic | Machine Learning Artificial Intelligence I.2.0; K.4.0 |
| url | https://arxiv.org/abs/2205.12961 |