从微控制器部署到深度学习，全面梳理边缘AI小型化演进
From Tiny Machine Learning to Tiny Deep Learning: A Survey
- 系统梳理微型机器学习向微型深度学习的转型路径
- 涵盖量化、剪枝、神经架构搜索等关键优化技术
- 适合边缘计算、嵌入式AI开发者及研究者参考
边缘设备的快速发展推动人工智能在边缘端部署的需求，催生了微型机器学习（TinyML）及其演进形态——微型深度学习（TinyDL）。TinyML最初聚焦于在微控制器上实现简单推理任务，而TinyDL标志着将深度学习模型部署到资源极度受限硬件上的范式转变。本综述全面回顾了从TinyML到TinyDL的演进，涵盖架构创新、硬件平台、模型优化技术与软件工具链。分析了当前最先进的量化、剪枝和神经架构搜索（NAS）方法，考察了从微控制器到专用神经加速器的硬件趋势。进一步分类了支持设备端学习的软件部署框架、编译器与AutoML工具。回顾了计算机视觉、音频识别、医疗健康与工业监控等领域的应用，展示TinyDL的实际影响。最后，指出新兴方向包括类脑计算、联邦式TinyDL、边缘原生基础模型及领域特定协同设计。本综述旨在为研究人员与实践者提供基础性资源，全面呈现边缘AI生态系统，为未来进展奠定基础。
原文摘要 · Abstract (English)
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from TinyML to TinyDL, encompassing architectural innovations, hardware platforms, model optimization techniques, and software toolchains. We analyze state-of-the-art methods in quantization, pruning, and neural architecture search (NAS), and examine hardware trends from MCUs to dedicated neural accelerators. Furthermore, we categorize software deployment frameworks, compilers, and AutoML tools enabling practical on-device learning. Applications across domains such as computer vision, audio recognition, healthcare, and industrial monitoring are reviewed to illustrate the real-world impact of TinyDL. Finally, we identify emerging directions including neuromorphic computing, federated TinyDL, edge-native foundation models, and domain-specific co-design approaches. This survey aims to serve as a foundational resource for researchers and practitioners, offering a holistic view of the ecosystem and laying the groundwork for future advancements in edge AI.
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