# FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation

arXiv: https://arxiv.org/abs/2310.11131 · HTML: https://mlsanity.com/paper/2310.11131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)

## Built on by (most cited first, 2 of 2)

- [Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference](https://mlsanity.com/paper/2406.14328/) (2024)
- [Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations](https://mlsanity.com/paper/2503.23934/) (2025)

## Abstract (arXiv)

The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, consumes 73% of its total energy. That makes it an ideal target for optimisation through the integration of Machine Learning (ML). However, the energy consumption of ML is frequently overlooked in such ecosystems. Our work addresses this critical aspect by presenting FROST - Flexible Reconfiguration method with Online System Tuning - a solution for energy-aware ML pipelines that adhere to O-RAN's specifications and principles. FROST is capable of profiling the energy consumption of an ML pipeline and optimising the hardware accordingly, thereby limiting the power draw. Our findings indicate that FROST can achieve energy savings of up to 26.4% without compromising the model's accuracy or introducing significant time delays.