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

arXiv: https://arxiv.org/abs/2501.14995 · HTML: https://mlsanity.com/paper/2501.14995/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Transformer models: an introduction and catalog](https://mlsanity.com/paper/2302.07730/) (2023)
- [Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices](https://mlsanity.com/paper/2310.18329/) (2023)
- [LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality](https://mlsanity.com/paper/2205.13770/) (2022)
- [Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications](https://mlsanity.com/paper/2011.13075/) (2020)
- [DeepEn2023: Energy Datasets for Edge Artificial Intelligence](https://mlsanity.com/paper/2312.00103/) (2023)

## Built on by (most cited first, 3 of 3)

- [lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models](https://mlsanity.com/paper/2510.06126/) (2025)
- [PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search](https://mlsanity.com/paper/2510.08993/) (2025)
- [Transferable Latency Prediction for Fast LLM Screening on Heterogeneous Edge Devices](https://mlsanity.com/paper/2607.21602/) (2026)

## Abstract (arXiv)

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.