Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of intelligent devices. In this work, a classification of the most common optimization techniques for Neural Network compression is conducted. Additionally, a review of the development boards and TinyML software is presented. Furthermore, the work provides educational resources, a classification of the technology applications, and future directions and concludes with the challenges and considerations.

微型机器学习 (TinyML) 是科学界提出的一项新兴技术，用于开发自主且安全的设备，这些设备可以收集、处理和提供结果，而无需将数据传输到外部实体。该技术旨在通过将人工智能应用于更多领域来实现人工智能的民主化，并为智能设备的数字革命做出贡献。在这项工作中，对神经网络压缩最常见的优化技术进行了分类。此外，还对开发板和 TinyML 软件进行了回顾。此外，这项工作还提供了教育资源、技术应用的分类和未来的方向，并以挑战和考虑作为结论。