The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms within small objects powered by Microcontroller Units (MCUs). This paves the way for the development of novel applications and services that do not need the omnipresent processing support from the cloud, which is power consuming and involves data security and privacy risks. In this work, a comprehensive review of the novel TinyML ecosystem is provided. The related challenges and opportunities are identified and the potential services that will be enabled by the development of truly smart frugal objects are discussed. As a main contribution of this paper, a detailed survey of the available TinyML frameworks for integrating ML algorithms within MCUs is provided. Besides, aiming at illustrating the given discussion, a real case study is presented. Concretely, we propose a multi-Radio Access Network (RAT) architecture for smart frugal objects. The issue of selecting the most adequate communication interface for sending sporadic messages considering both the status of the device and the characteristics of the data to be sent is addressed. To this end, several TinyML frameworks are evaluated and the performances of a number of ML algorithms embedded in an Arduino Uno board are analyzed. The attained results reveal the validity of the TinyML approach, which successfully enables the integration of techniques such as Neural Networks (NNs), Support Vector Machine (SVM), decision trees, or Random Forest (RF) in frugal objects with constrained hardware resources. The outcomes also show promising results in terms of algorithm's accuracy and computation performance.

中文翻译：

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支持 TinyML 的节俭智能对象：挑战和机遇

TinyML 范式建议将基于机器学习 (ML) 的机制集成到由微控制器单元 (MCU) 驱动的小对象中。这为开发新的应用程序和服务铺平了道路，这些应用程序和服务不需要来自云的无处不在的处理支持，而云是耗电的并且涉及数据安全和隐私风险。在这项工作中，提供了对新型 TinyML 生态系统的全面审查。确定了相关的挑战和机遇，并讨论了将通过开发真正智能的节俭对象实现的潜在服务。作为本文的主要贡献，提供了对可用于在 MCU 中集成 ML 算法的可用 TinyML 框架的详细调查。此外，为了说明给定的讨论，还提供了一个真实的案例研究。具体来说，我们为智能节俭的对象提出了一种多无线电接入网络（RAT）架构。考虑到设备的状态和要发送的数据的特性，选择最合适的通信接口来发送零星消息的问题得到了解决。为此，评估了几个 TinyML 框架，并分析了嵌入 Arduino Uno 板的许多 ML 算法的性能。获得的结果揭示了 TinyML 方法的有效性，该方法成功地在硬件资源受限的节俭对象中集成了神经网络 (NN)、支持向量机 (SVM)、决策树或随机森林 (RF) 等技术。结果在算法的准确性和计算性能方面也显示出有希望的结果。