The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

Abstract: 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.