人工智能生命周期在通信网络中的能耗
The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks
AI总结：
本文提出了一种衡量人工智能在通信网络生命周期中能耗的新指标eCAL，通过分析数据收集和处理复杂性，量化模型部署和推理过程中的能耗变化。
摘要：
人工智能（AI）正被应用于多个优化、调度、编排以及原生通信网络功能中。这种范式转变导致了能耗的增加，然而，量化将智能添加到通信系统中的端到端能耗仍是一个开放性挑战，因为传统能耗指标只关注通信、计算基础设施或模型开发。为了解决这个问题，我们提出了一种新的度量标准，即人工智能生命周期的能耗（eCAL）。eCAL捕捉了人工智能模型在系统中进行开发、部署和使用时的能耗，通过（i）分析各个组件中数据收集和处理的复杂性，以及（ii）推导总体和每比特能耗。我们证明，随着训练好的AI模型被更频繁地用于推理，其每次推理的能耗会降低，因为固定的训练能耗会随着推理次数的增加而摊销。对于一个简单的案例研究，我们显示，100次推理的eCAL是1000次推理的2.73倍。此外，我们还开发了一个模块化且可扩展的开源仿真工具，使研究人员、从业者和工程师能够通过各种配置和不同系统计算端到端能耗，确保适应不同的应用场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
