Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of executing arbitrary models on arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of IoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of various models, on a wide variety of low-power IoT boards, based on popular microcontroller architectures. U-TOE allows easily reproducible and customizable comparative evaluation experiments on a wide variety of IoT hardware all-at-once. The availability of a toolkit such as U-TOE is desirable to accelerate research combining Artificial Intelligence and IoT towards fully exploiting the potential of edge computing.

翻译：TinyML社区的研究结果表明，即使是在基于微控制器的小型终端设备上，也能直接执行机器学习模型。然而，目前该领域的从业者缺乏便捷的一体化工具包来评估在任意低功耗物联网硬件上运行任意模型的可行性。为此，本文提出U-TOE——一种通用工具包，它通过整合低功耗嵌入式操作系统、通用模型转译器与编译器、集成性能测量模块以及开放远程物联网测试平台的功能，旨在简化物联网设计人员与研究人员的任务。我们提供了U-TOE的开源实现，并展示了如何利用它在基于主流微控制器架构的多种低功耗物联网开发板上实验评估不同模型的性能。U-TOE能够同时支持在各种物联网硬件上开展易于复现且可定制的对比评估实验。此类工具包的可得性对于加速人工智能与物联网的交叉研究、充分挖掘边缘计算潜力具有重要意义。