Lightweight Deep Learning for Resource-Constrained Environments: A Survey
ACM Computing Surveys(2024)
摘要
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.
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