# Normalizing Energy Consumption for Hardware-Independent Evaluation

arXiv: https://arxiv.org/abs/2409.05602 · HTML: https://mlsanity.com/paper/2409.05602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Energy Consumption of Deep Generative Audio Models](https://mlsanity.com/paper/2107.02621/) (2021)
- [Dissecting FLOPs along input dimensions for GreenAI cost estimations](https://mlsanity.com/paper/2107.11949/) (2021)
- [AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer](https://mlsanity.com/paper/2303.03689/) (2023)
- [Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models](https://mlsanity.com/paper/2304.00897/) (2023)
- [Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system](https://mlsanity.com/paper/2210.07856/) (2022)
- [Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems](https://mlsanity.com/paper/2310.03455/) (2023)

## Built on by (most cited first, 1 of 1)

- [Energy Consumption Trends in Sound Event Detection Systems](https://mlsanity.com/paper/2409.08763/) (2024)

## Abstract (arXiv)

The increasing use of machine learning (ML) models in signal processing has raised concerns about their environmental impact, particularly during resource-intensive training phases. In this study, we present a novel methodology for normalizing energy consumption across different hardware platforms to facilitate fair and consistent comparisons. We evaluate different normalization strategies by measuring the energy used to train different ML architectures on different GPUs, focusing on audio tagging tasks. Our approach shows that the number of reference points, the type of regression and the inclusion of computational metrics significantly influences the normalization process. We find that the appropriate selection of two reference points provides robust normalization, while incorporating the number of floating-point operations and parameters improves the accuracy of energy consumption predictions. By supporting more accurate energy consumption evaluation, our methodology promotes the development of environmentally sustainable ML practices.