The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

翻译：微型机器学习（TinyML）的兴起通过促进资源受限的物联网硬件设备及其基于学习的软件架构的协同设计，积极推动了人工智能领域的革命。TinyML在第四次和第五次工业革命中扮演着关键角色，帮助社会、经济体和个人采用有效的人工智能赋能计算技术（例如智能城市、汽车和医疗机器人）。鉴于其多学科性质，TinyML领域已从多个角度得到探讨：本综合性综述旨在提供一份聚焦于TinyML解决方案中所有学习算法的最新概述。该综述基于系统综述和荟萃分析的首选报告项目（PRISMA）方法学流程，从而实现系统而完整的文献调查。具体而言，我们首先考察实施基于TinyML系统的三种不同工作流程，即面向ML、面向硬件和协同设计。其次，我们提出一个从TinyML视角覆盖学习全景的分类法，详细审视不同类型的模型优化与设计，以及最先进的学习技术。第三，本综述将介绍代表当前TinyML智能边缘应用最先进水平的硬件设备和软件工具的独特特征。最后，我们讨论挑战与未来方向。