The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks
This addresses the problem of energy inefficiency in AI-driven communication networks for researchers and engineers, though it is incremental as it builds on existing energy metrics by extending them to the AI lifecycle.
The paper tackles the challenge of quantifying the end-to-end energy consumption of AI in communication networks by proposing a new metric called eCAL, which captures energy across the AI lifecycle and shows that energy cost per inference decreases with more uses, with a case study indicating eCAL for 100 inferences is 2.73 times higher than for 1000 inferences.
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.