论文标题
极端边缘的情报：可改革的调查
Intelligence at the Extreme Edge: A Survey on Reformable TinyML
论文作者
论文摘要
微型机器学习（Tinyml）是一个令人振奋的研究领域，它建议将机器学习的使用和深度学习对高能节能的节俭微控制器单元进行民主化。考虑到Tinyml只能进行推断的总体假设，对该领域的兴趣越来越大，这导致了使它们可以改革的工作，即允许模型一旦部署的解决方案。这项工作通过新的分类法提出了有关可改革Tinyml解决方案的调查。在这里，讨论了每个层次结构对改革性的适用性。此外，我们探索了Tinyml的工作流程，并分析已确定的部署方案，可用工具以及几乎没有可用的基准测试工具。最后，我们讨论可改革的蒂尼尔尔如何影响一些选定的工业领域，并讨论挑战和未来的方向。
Tiny 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. 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.
