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arXiv · 2004.11250

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
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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.

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Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren. 2020-04-22. Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization. https://arxiv.org/abs/2004.11250

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