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)和机器学习(ML)受到了工业和学术界的极大关注。特别是,传统的ML技术需要大量的电力来满足所需的准确性,这主要限制了它们的使用范围,主要是高性能设备,如网络节点。然而,随着物联网(IoT)和边缘计算等技术的许多进步,将ML技术应用于资源受限的嵌入式设备以实现分布式和普适性智能变得十分有必要。这就催生了TinyML范式,它是一种嵌入式ML技术,可以在多个廉价、资源和能源受限的设备上实现ML应用。然而,在这种向适当实现TinyML技术的转变过程中,多个挑战,如处理能力优化、可靠性改进和维护学习模型的准确性,需要及时解决。本文回顾了实现TinyML的各种途径。首先,提供了TinyML的背景,然后详细讨论了支持TinyML的各种工具。随后,介绍了利用先进技术实现TinyML的最新应用。最后,确定了各种研究挑战和未来方向。