GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices

Xiaolong Tu, Dawei Chen, Kyungtae Han, Onur Altintas, Haoxin Wang
1/25/2025
cs.LG

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Abstract

We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based search method within an expanded neural architecture search (NAS) space, guided by gradient descent to optimize model exploration. Pre-trained kernel-level energy predictors estimate energy consumption across all models, providing a global view that directs the search toward more sustainable solutions. By automating performance measurements and iteratively refining the search process, GreenAuto demonstrates the efficient identification of sustainable AI models without the need for human intervention.

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Cite this paper

@article{tu2025greenauto,
  title  = {GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices},
  author = {Xiaolong Tu and Dawei Chen and Kyungtae Han and Onur Altintas and Haoxin Wang},
  year   = {2025},
  eprint = {2501.14995},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.14995v1}
}

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