Artificial intelligence (AI)coupled with existing Internet of Things (IoT) enables more streamlined and autonomous operations across various economic sectors. Consequently, the paradigm of Artificial Intelligence of Things (AIoT) having AI techniques at its core implies additional energy and carbon costs that may become significant with more complex neural architectures. To better understand the energy and Carbon Footprint (CF) of some AIoT components, very recent studies employ conventional metrics. However, these metrics are not designed to capture energy efficiency aspects of inference. In this paper, we propose a new metric, the Energy Cost of AIoT Lifecycle (eCAL) to capture the overall energy cost of inference over the lifecycle of an AIoT system. We devise a new methodology for determining eCAL of an AIoT system by analyzing the complexity of data manipulation in individual components involved in the AIoT lifecycle and derive the overall and per bit energy consumption. With eCAL we show that the better a model is and the more it is used, the more energy efficient an inference is. For an example AIoT configuration, eCAL for making $100$ inferences is $1.43$ times higher than for $1000$ inferences. We also evaluate the CF of the AIoT system by calculating the equivalent CO$_{2}$ emissions based on the energy consumption and the Carbon Intensity (CI) across different countries. Using 2023 renewable data, our analysis reveals that deploying an AIoT system in Germany results in emitting $4.62$ times higher CO$_2$ than in Finland, due to latter using more low-CI energy sources.

翻译：人工智能（AI）与现有物联网（IoT）的结合，能够在多个经济领域实现更精简和自主的运营。因此，以AI技术为核心的人工智能物联网（AIoT）范式意味着额外的能源和碳成本，随着神经网络架构日益复杂，这些成本可能变得相当可观。为了更好地理解某些AIoT组件的能源消耗和碳足迹（CF），近期研究采用了传统指标。然而，这些指标并非为捕捉推理过程的能效方面而设计。本文提出一种新指标——人工智能物联网生命周期能源成本（eCAL），用以捕捉AIoT系统在其整个生命周期内进行推理的总体能源成本。我们设计了一种新方法来确定AIoT系统的eCAL，通过分析AIoT生命周期中各个组件数据处理的复杂性，推导出总体及每比特的能耗。利用eCAL，我们表明模型性能越好、使用越频繁，单次推理的能效就越高。例如，在一个示例AIoT配置中，进行$100$次推理的eCAL比进行$1000$次推理高出$1.43$倍。我们还通过基于能耗和不同国家的碳强度（CI）计算等效CO$_{2}$排放量，评估了该AIoT系统的CF。利用2023年可再生能源数据，我们的分析表明，在德国部署一个AIoT系统所产生的CO$_2$排放量是在芬兰部署的$4.62$倍，这主要是因为芬兰使用了更多低碳强度的能源。