In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.

翻译：近年来，人工智能（AI）与机器学习（ML）在工业界和学术界引起了广泛关注。值得注意的是，传统ML技术需消耗大量计算资源以满足预期精度，这使其应用主要局限于网络节点等高能力设备。然而，随着物联网（IoT）和边缘计算等技术领域的诸多进步，将ML技术融入资源受限的嵌入式设备以实现分布式和普适智能的需求日益迫切。这催生了TinyML范式的兴起，这是一种嵌入式ML技术，能够使ML应用在多个低成本、资源和功率受限的设备上运行。然而，在向TinyML技术适当实施过渡的过程中，处理能力优化、可靠性提升以及学习模型精度的保持等多项挑战亟待解决。本文综述了TinyML实施的各种可行途径。首先，介绍了TinyML的背景知识，随后详细讨论了支持TinyML的各种工具。接着，详细阐述了利用先进技术的TinyML最新应用。最后，指出了各类研究挑战和未来发展方向。