NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

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 task...

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Vydané v:2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autori: Yu, Zhongzhi, Fu, Yonggan, Yuan, Jiayi, You, Haoran, Lin, Yingyan Celine
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 09.07.2023
Predmet:
Boosting
Deep learning
Design automation
Efficient Deep Learning
Neural networks
Task analysis
Tiny Neural Networks
Training
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Shrnutí: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.
DOI:10.1109/DAC56929.2023.10247827