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

arXiv: https://arxiv.org/abs/2004.11250 · HTML: https://mlsanity.com/paper/2004.11250/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores](https://mlsanity.com/paper/2106.12169/) (2021)

## Abstract (arXiv)

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.