The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

Abstract

Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. While this paradigm shift results in increased energy consumption, quantifying the end-toend energy consumption of adding intelligence to such systems is particularly challenging. Conventional 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 one AI model in a system. eCAL captures the energy consumption throughout the development and deployment of an AI-model providing intelligence in a wireless communication network by analyzing the complexity of data collection and manipulation in individual components and deriving overall and per-bit energy consumption. We show that the better a model is and the more it is used, the more energy efficient an inference is. For a simple case study, eCAL for making 100 inferences is 2.73 times higher than for 1000 inferences. Additionally, we have developed a modular and extendable opensource 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.

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