A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations
Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …
massive model sizes that require significant computational and storage resources. To …
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …
range of real-world vision and language processing tasks, spanning from image …
LR-CNN: Lightweight Row-centric Convolutional Neural Network Training for Memory Reduction
Z Wang, H Yang, N Wang, C Xu, J Nie, Z Wei… - arXiv preprint arXiv …, 2024 - arxiv.org
In the last decade, Convolutional Neural Network with a multi-layer architecture has
advanced rapidly. However, training its complex network is very space-consuming, since a …
advanced rapidly. However, training its complex network is very space-consuming, since a …
Rethinking Imbalance in Image Super-Resolution for Efficient Inference
Existing super-resolution (SR) methods optimize all model weights equally using $\mathcal
{L} _1 $ or $\mathcal {L} _2 $ losses by uniformly sampling image patches without …
{L} _1 $ or $\mathcal {L} _2 $ losses by uniformly sampling image patches without …