Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

• URL: http://arxiv.org/abs/2004.11250v1
• Date: Wed, 22 Apr 2020 03:18:23 GMT
• Title: Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
• Authors: Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren
• Abstract summary: High-end mobile platforms serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. constrained computation and storage resources on these devices pose significant challenges for real-time inference executions. We propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on mobile devices.
• Score: 56.3111706960878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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