Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model’s accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas.

在过去的十年中，深度学习在人工智能的各个领域占据主导地位，包括自然语言处理、计算机视觉和生物医学信号处理。尽管模型精度有了显着提高，但在移动电话和微控制器等轻量级设备上部署这些模型却受到资源有限的限制。在本次调查中，我们提供了针对这些设备量身定制的全面设计指南，详细介绍了轻量级模型、压缩方法和硬件加速策略的精心设计。这项工作的主要目标是探索在不影响模型准确性的情况下绕过硬件限制的方法和概念。此外，我们还探索了未来轻量级深度学习的两条值得注意的路径：TinyML 和大型语言模型的部署技术。尽管这些路径无疑具有潜力，但它们也提出了重大挑战，鼓励对未探索领域的研究。