The deployment of Quantized Neural Networks (QNNs) on resource-constrained edge devices, such as microcontrollers (MCUs), introduces fundamental challenges in balancing model performance, computational complexity, and memory constraints. Tiny Machine Learning (TinyML) addresses these issues by jointly advancing machine learning algorithms, hardware architectures, and software optimization techniques to enable deep neural network inference on embedded systems. This survey provides a hardware-oriented perspective on neural network quantization, systematically reviewing the quantization methods most relevant to MCUs and extreme-edge devices. Particular emphasis is placed on the critical trade-offs between model performance and the capabilities of MCU-class hardware, including memory hierarchies, numerical representations, and accelerator support. The survey further reviews contemporary MCU hardware platforms, including ARM-based and RISC-V-based designs, as well as MCUs integrating neural processing units (NPUs) for low-precision inference, together with the supporting software stacks. In addition, we analyze real-world deployments of quantized models on MCUs and consolidate the application domains in which such systems are used. Finally, we discuss open challenges and outline promising future directions toward scalable, energy-efficient, and sustainable AI deployment on edge devices.

翻译：在资源受限的边缘设备（如微控制器）上部署量化神经网络，需要在模型性能、计算复杂度与内存限制之间取得平衡，这带来了根本性挑战。微型机器学习通过协同推进机器学习算法、硬件架构与软件优化技术，以实现在嵌入式系统上进行深度神经网络推理。本综述从硬件导向的视角探讨神经网络量化，系统性地回顾了与微控制器及极边缘设备最相关的量化方法。特别聚焦于模型性能与微控制器级硬件能力之间的关键权衡，包括内存层次结构、数值表示及加速器支持。本综述进一步评述了当代微控制器硬件平台，涵盖基于ARM和RISC-V的设计，以及集成神经处理单元以支持低精度推理的微控制器，并分析了其配套软件栈。此外，我们剖析了量化模型在微控制器上的实际部署案例，并归纳了此类系统的应用领域。最后，我们讨论了当前面临的开放挑战，并展望了在边缘设备上实现可扩展、高能效及可持续人工智能部署的未来研究方向。