# The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

arXiv: https://arxiv.org/abs/2408.00540 · HTML: https://mlsanity.com/paper/2408.00540/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [KAN or MLP: A Fairer Comparison](https://mlsanity.com/paper/2407.16674/) (2024)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies](https://mlsanity.com/paper/2104.00972/) (2021)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [Towards Sustainable Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2201.09071/) (2022)
- [Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services](https://mlsanity.com/paper/2304.06414/) (2023)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [An Overview and Solution for Democratizing AI Workflows at the Network Edge](https://mlsanity.com/paper/2407.11905/) (2024)
- [Learning Wireless Data Knowledge Graph for Green Intelligent Communications: Methodology and Experiments](https://mlsanity.com/paper/2404.10365/) (2024)

## Built on by (most cited first, 4 of 4)

- [Automated Modeling Method for Pathloss Model Discovery](https://mlsanity.com/paper/2505.23383/) (2025)
- [SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator](https://mlsanity.com/paper/2510.26340/) (2025)
- [Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation](https://mlsanity.com/paper/2604.05520/) (2026)
- [Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum](https://mlsanity.com/paper/2609.18283/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. This paradigm shift results in increased energy consumption, however, quantifying the end-to-end energy consumption of adding intelligence to communication systems remains an open challenge since conventional energy consumption metrics focus on either communication, computation infrastructure, or model development. To address this, we propose a new metric, the Energy Cost of AI Lifecycle (eCAL) of an AI model in a system. eCAL captures the energy consumption throughout the development, deployment and utilization of an AI-model providing intelligence in a communication network by (i) analyzing the complexity of data collection and manipulation in individual components and (ii) deriving overall and per-bit energy consumption. We show that as a trained AI model is used more frequently for inference, its energy cost per inference decreases, since the fixed training energy is amortized over a growing number of inferences. For a simple case study we show that eCAL for 100 inferences is 2.73 times higher than for 1000 inferences. Additionally, we have developed a modular and extendable open-source simulation tool to enable researchers, practitioners, and engineers to calculate the end-to-end energy cost with various configurations and across various systems, ensuring adaptability to diverse use cases.