Lightweight Deep Learning for Resource-Constrained Environments: A Survey

arXiv:2404.07236v1 Announce Type: cross
Abstract: 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 …

abstract accuracy artificial artificial intelligence arxiv biomedical computer computer vision cs.cv cs.lg deep learning devices domains environments improvements intelligence language language processing mobile mobile phones model accuracy natural natural language natural language processing phones processing signal survey type vision