The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters. There are many studies corroborating these trends, but does this translate into an exponential increase in energy consumption? In order to answer this question we focus on inference costs rather than training costs, as the former account for most of the computing effort, solely because of the multiplicative factors. Also, apart from algorithmic innovations, we account for more specific and powerful hardware (leading to higher FLOPS) that is usually accompanied with important energy efficiency optimisations. We also move the focus from the first implementation of a breakthrough paper towards the consolidated version of the techniques one or two year later. Under this distinctive and comprehensive perspective, we study relevant models in the areas of computer vision and natural language processing: for a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated. The only caveat is, yet again, the multiplicative factor, as future AI increases penetration and becomes more pervasive.

翻译：深度学习等人工智能范式的进步被认为与参数数量的指数增长相关联。已有大量研究证实这一趋势，但这是否意味着能耗的指数级增长？为回答这一问题，我们聚焦推理成本而非训练成本，因为前者仅因乘法效应就占据了大部分计算量。此外，除了算法创新，我们还考虑了更专门且更强大的硬件（带来更高的FLOPS），这类硬件通常伴随重要的能效优化。同时，我们将关注点从突破性论文的首版实现转向一两年后技术的成熟版本。基于这一独特且全面的视角，我们研究了计算机视觉与自然语言处理领域的关键模型：在性能持续提升的情况下，我们观察到能耗的增长远低于先前的预期。唯一的特例仍然是乘法效应——随着未来AI渗透率提高并变得更加普及，这一因素将愈发凸显。