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，深度学习在自然语言处理、计算机视觉和生物医学信号处理等各个人工智能领域中占据主导地位。虽然模型的准确性有了显著提高，但将这些模型部署在移动手机和微控制器等轻量设备上受到资源限制的制约。本调查报告针对这些设备提供全面的设计指导，详细介绍了轻量模型的细致设计、模型压缩方法和硬件加速策略。本工作的主要目标是探索在不影响模型准确性的情况下克服硬件限制的方法和概念。此外，我们还探讨了未来轻量深度学习的两个值得关注的方向：TinyML 和大型语言模型的部署技术。尽管这些方向无疑具有潜力，但它们也带来了重大挑战，鼓励研究未开拓的领域。