The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML) has emerged as a key enabler of this evolution, facilitating the deployment of ML models on devices such as microcontrollers and embedded systems. However, the complexity of managing the TinyML lifecycle, including stages such as data processing, model optimization and conversion, and device deployment, presents significant challenges and often requires substantial human intervention. Motivated by these challenges, we began exploring whether Large Language Models (LLMs) could help automate and streamline the TinyML lifecycle. We developed a framework that leverages the natural language processing (NLP) and code generation capabilities of LLMs to reduce development time and lower the barriers to entry for TinyML deployment. Through a case study involving a computer vision classification model, we demonstrate the framework's ability to automate key stages of the TinyML lifecycle. Our findings suggest that LLM-powered automation holds potential for improving the lifecycle development process and adapting to diverse requirements. However, while this approach shows promise, there remain obstacles and limitations, particularly in achieving fully automated solutions. This paper sheds light on both the challenges and opportunities of integrating LLMs into TinyML workflows, providing insights into the path forward for efficient, AI-assisted embedded system development.

翻译：物联网应用需求的不断演进正推动智能技术日益向边缘端迁移，以实现资源受限环境下的实时洞察与决策。微型机器学习已成为这一演进的关键赋能技术，促进了机器学习模型在微控制器和嵌入式系统等设备上的部署。然而，管理TinyML生命周期的复杂性——包括数据处理、模型优化与转换、设备部署等阶段——带来了重大挑战，通常需要大量人工干预。受这些挑战的驱动，我们开始探索大型语言模型能否帮助实现TinyML生命周期的自动化与流程优化。我们开发了一个框架，利用LLMs的自然语言处理与代码生成能力，以缩短开发时间并降低TinyML部署的门槛。通过一个涉及计算机视觉分类模型的案例研究，我们展示了该框架在自动化TinyML生命周期关键阶段的能力。研究结果表明，基于LLM的自动化技术具有改进生命周期开发流程并适应多样化需求的潜力。然而，尽管该方法展现出前景，但在实现完全自动化解决方案方面仍存在障碍与局限。本文揭示了将LLMs整合到TinyML工作流中面临的挑战与机遇，为高效、人工智能辅助的嵌入式系统开发提供了前瞻性见解。