The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

TL;DR

This paper introduces the Energy Cost of AI Lifecycle (eCAL), a new metric to quantify the energy consumption of AI models in communication networks throughout their development, deployment, and use, addressing a key gap in existing metrics.

Contribution

The paper proposes the eCAL metric, analyzes its components, and provides a modular simulation tool for end-to-end energy cost calculation in communication systems.

Findings

Energy cost per inference decreases with more inferences due to amortized training energy.

eCAL for 100 inferences is 2.73 times higher than for 1000 inferences.

The open-source tool enables customizable energy consumption analysis.

Abstract

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.