The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like quantization and distillation have further expanded PTM applicability to resource-constrained IoT hardware. Given the many PTM options for any given task, engineers often find it too costly to evaluate each model's suitability. Approaches such as LogME, LEEP, and ModelSpider help streamline model selection by estimating task relevance without exhaustive tuning. However, these methods largely leave hardware constraints as future work-a significant limitation in IoT settings. In this paper, we identify the limitations of current model recommendation approaches regarding hardware constraints and introduce a novel, hardware-aware method for PTM selection. We also propose a research agenda to guide the development of effective, hardware-conscious model recommendation systems for IoT applications.

翻译：预训练模型（PTMs）的可用性通过减少对大量训练的需求，促进了机器学习在各应用领域的快速部署。量化与蒸馏等技术进一步扩展了PTMs在资源受限的物联网硬件上的适用性。鉴于针对任何给定任务存在众多PTM选项，工程师通常发现评估每个模型的适用性成本过高。诸如LogME、LEEP和ModelSpider等方法通过无需详尽调优即可估计任务相关性，有助于简化模型选择过程。然而，这些方法大多将硬件约束留作未来工作——这在物联网应用场景中是一个显著局限。本文指出了当前模型推荐方法在硬件约束方面的局限性，并提出了一种新颖的、硬件感知的PTM选择方法。我们还提出了一个研究议程，以指导为物联网应用开发高效且具备硬件意识的模型推荐系统。