# Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments

arXiv: https://arxiv.org/abs/2408.15128 · HTML: https://mlsanity.com/paper/2408.15128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 150 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Energy Usage Reports: Environmental awareness as part of algorithmic accountability](https://mlsanity.com/paper/1911.08354/) (2019)
- [NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1710.05420/) (2017)
- [Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models](https://mlsanity.com/paper/2304.00897/) (2023)
- [Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning](https://mlsanity.com/paper/2107.06946/) (2021)
- [BoolNet: Minimizing The Energy Consumption of Binary Neural Networks](https://mlsanity.com/paper/2106.06991/) (2021)
- [A first look into the carbon footprint of federated learning](https://mlsanity.com/paper/2102.07627/) (2021)
- [Energy of Computing on Multicore CPUs: Predictive Models and Energy Conservation Law](https://mlsanity.com/paper/1907.02805/) (2019)
- [Performance and Energy Consumption of Parallel Machine Learning Algorithms](https://mlsanity.com/paper/2305.00798/) (2023)

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

- [Small is Sufficient: Reducing the World AI Energy Consumption Through Model Selection](https://mlsanity.com/paper/2510.01889/) (2025)
- [WattLayer: Get Layers Right to Estimate Inference Energy of Neural Networks](https://mlsanity.com/paper/2606.27841/) (2026)
- [LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations](https://mlsanity.com/paper/2608.18503/) (2026)

## Abstract (arXiv)

Monitoring, understanding, and optimizing the energy consumption of Machine Learning (ML) are various reasons why it is necessary to evaluate the energy usage of ML. However, there exists no universal tool that can answer this question for all use cases, and there may even be disagreement on how to evaluate energy consumption for a specific use case. Tools and methods are based on different approaches, each with their own advantages and drawbacks, and they need to be mapped out and explained in order to select the most suitable one for a given situation. We address this challenge through two approaches. First, we conduct a systematic literature review of all tools and methods that permit to evaluate the energy consumption of ML (both at training and at inference), irrespective of whether they were originally designed for machine learning or general software. Second, we develop and use an experimental protocol to compare a selection of these tools and methods. The comparison is both qualitative and quantitative on a range of ML tasks of different nature (vision, language) and computational complexity. The systematic literature review serves as a comprehensive guide for understanding the array of tools and methods used in evaluating energy consumption of ML, for various use cases going from basic energy monitoring to consumption optimization. Two open-source repositories are provided for further exploration. The first one contains tools that can be used to replicate this work or extend the current review. The second repository houses the experimental protocol, allowing users to augment the protocol with new ML computing tasks and additional energy evaluation tools.