The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment of deep learning models on ultra-low power microcontrollers (MCUs) for TinyML applications. It begins by introducing neural networks and discussing their architectures and resource requirements. It then explores MEMS-based applications on ultra-low power MCUs, highlighting their potential for enabling TinyML on resource-constrained devices. The core of the review centres on efficient neural networks for TinyML. It covers techniques such as model compression, quantization, and low-rank factorization, which optimize neural network architectures for minimal resource utilization on MCUs. The paper then delves into the deployment of deep learning models on ultra-low power MCUs, addressing challenges such as limited computational capabilities and memory resources. Techniques like model pruning, hardware acceleration, and algorithm-architecture co-design are discussed as strategies to enable efficient deployment. Lastly, the review provides an overview of current limitations in the field, including the trade-off between model complexity and resource constraints. Overall, this review paper presents a comprehensive analysis of efficient neural networks and deployment strategies for TinyML on ultra-low-power MCUs. It identifies future research directions for unlocking the full potential of TinyML applications on resource-constrained devices.

翻译：微型机器学习领域因能在资源受限设备上实现智能应用而备受关注。本综述深入分析了面向微小型机器学习应用的神经网络效率优化及深度学习模型在超低功耗微控制器上的部署进展。首先介绍神经网络的基本原理，探讨其架构设计与资源需求特征。继而阐述基于微机电系统的超低功耗微控制器应用场景，揭示其在资源受限设备上实现微型机器学习的潜力。综述核心聚焦于面向微型机器学习的神经网络效率优化，涵盖模型压缩、量化、低秩分解等技术手段——这些方法通过优化神经网络架构以实现微控制器上资源利用最小化。针对超低功耗微控制器上部署深度学习模型所面临的计算能力与存储资源限制等挑战，本文重点讨论了模型剪枝、硬件加速及算法-架构协同设计等部署策略。最后，系统梳理了当前领域存在的模型复杂度与资源约束之间权衡等局限性。总体而言，本综述全面分析了面向超低功耗微控制器微型机器学习的神经网络效率优化与部署策略，指明了在资源受限设备上释放微型机器学习应用潜能的未来研究方向。