IJCAI 2020poster0 citations
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren
Abstract
High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage resources on these devices still pose significant challenges for real-time DNN inference executions. To address this problem, we propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on mobile devices. This demo shows that these optimizations can enable real-time mobile execution of multiple DNN applications, including style transfer, DNN coloring and super resolution.
Computer Vision: generalMachine Learning: general
BibTeX
@inproceedings{ijcai2020p778,
title = {Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization},
author = {Niu, Wei and Zhao, Pu and Zhan, Zheng and Lin, Xue and Wang, Yanzhi and Ren, Bin},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5306--5308},
year = {2020},
month = {7},
note = {Demos},
doi = {10.24963/ijcai.2020/778},
url = {https://doi.org/10.24963/ijcai.2020/778},
}