Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913788717957120 |
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| author | La, Hoang-Loc Ha, Phuong Hoai |
| author_facet | La, Hoang-Loc Ha, Phuong Hoai |
| contents | Many studies estimate energy consumption using proxy metrics like memory usage, FLOPs, and inference latency, with the assumption that reducing these metrics will also lower energy consumption in neural networks. This paper, however, takes a different approach by introducing an energy-efficient Neural Architecture Search (NAS) method that directly focuses on identifying architectures that minimize energy consumption while maintaining acceptable accuracy. Unlike previous methods that primarily target vision and language tasks, the approach proposed here specifically addresses tabular datasets. Remarkably, the optimal architecture suggested by this method can reduce energy consumption by up to 92% compared to architectures recommended by conventional NAS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset La, Hoang-Loc Ha, Phuong Hoai Machine Learning Artificial Intelligence Many studies estimate energy consumption using proxy metrics like memory usage, FLOPs, and inference latency, with the assumption that reducing these metrics will also lower energy consumption in neural networks. This paper, however, takes a different approach by introducing an energy-efficient Neural Architecture Search (NAS) method that directly focuses on identifying architectures that minimize energy consumption while maintaining acceptable accuracy. Unlike previous methods that primarily target vision and language tasks, the approach proposed here specifically addresses tabular datasets. Remarkably, the optimal architecture suggested by this method can reduce energy consumption by up to 92% compared to architectures recommended by conventional NAS. |
| title | Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08359 |