Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units (MCUs). Considering the general assumption that TinyML can only run inference, growing interest in the domain has led to work that makes them reformable, i.e., solutions that permit models to improve once deployed. This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy. Here, the suitability of each hierarchical layer for reformability is discussed. Furthermore, we explore the workflow of TinyML and analyze the identified deployment schemes, available tools, and the scarcely available benchmarking tools. Finally, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges, and future directions, and its fusion with next-generation AI.

机器学习 (TinyML) 是一个新兴的研究领域，旨在使机器学习和深度学习在高能效、节俭的微控制器单元 (MCU) 上的使用民主化。考虑到 TinyML 只能运行推理的一般假设，对该领域日益增长的兴趣导致了使它们可改革的工作，即允许模型在部署后进行改进的解决方案。这项工作提出了一项关于可改革 TinyML 解决方案的调查，并提出了一种新颖的分类法。这里，讨论了每个层级对于可重构性的适用性。此外，我们探索了 TinyML 的工作流程，并分析了已确定的部署方案、可用工具和几乎不可用的基准测试工具。最后，我们讨论可改革的 TinyML 如何影响一些选定的工业领域并讨论挑战，