Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions.
@article{arxiv.2306.13586,
title = {NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants},
author = {Zhongzhi Yu and Yonggan Fu and Jiayi Yuan and Haoran You and Yingyan Lin},
journal= {arXiv preprint arXiv:2306.13586},
year = {2023}
}