# An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

arXiv: https://arxiv.org/abs/2001.07710 · HTML: https://mlsanity.com/paper/2001.07710/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [SPViT: Enabling Faster Vision Transformers via Soft Token Pruning](https://mlsanity.com/paper/2112.13890/) (2021)
- [MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://mlsanity.com/paper/2110.14032/) (2021)
- [Advancing Model Pruning via Bi-level Optimization](https://mlsanity.com/paper/2210.04092/) (2022)
- [SparCL: Sparse Continual Learning on the Edge](https://mlsanity.com/paper/2209.09476/) (2022)
- [Teachers Do More Than Teach: Compressing Image-to-Image Models](https://mlsanity.com/paper/2103.03467/) (2021)
- [F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization](https://mlsanity.com/paper/2202.05239/) (2022)
- [Effective Model Sparsification by Scheduled Grow-and-Prune Methods](https://mlsanity.com/paper/2106.09857/) (2021)
- [Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers](https://mlsanity.com/paper/2302.02596/) (2023)
- [Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training](https://mlsanity.com/paper/2209.11204/) (2022)
- [Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration](https://mlsanity.com/paper/2111.11581/) (2021)
- [On Accelerating Edge AI: Optimizing Resource-Constrained Environments](https://mlsanity.com/paper/2501.15014/) (2025)
- [Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices](https://mlsanity.com/paper/2210.08578/) (2022)
- [Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models](https://mlsanity.com/paper/2602.01167/) (2026)
- [Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks](https://mlsanity.com/paper/2112.10898/) (2021)
- [Can abstract concepts from LLM improve SLM performance?](https://mlsanity.com/paper/2512.19069/) (2025)
- [Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models](https://mlsanity.com/paper/2603.05582/) (2026)
- [Neural Network Compression by Approximate Differential Equivalence](https://mlsanity.com/paper/2606.01402/) (2026)

## Abstract (arXiv)

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limited on-device acceleration performance. To solve the problem, we introduce a new sparsity dimension, namely pattern-based sparsity that comprises pattern and connectivity sparsity, and becoming both highly accurate and hardware friendly. With carefully designed patterns, the proposed pruning unprecedentedly and consistently achieves accuracy enhancement and better feature extraction ability on different DNN structures and datasets, and our pattern-aware pruning framework also achieves pattern library extraction, pattern selection, pattern and connectivity pruning and weight training simultaneously. Our approach on the new pattern-based sparsity naturally fits into compiler optimization for highly efficient DNN execution on mobile platforms. To the best of our knowledge, it is the first time that mobile devices achieve real-time inference for the large-scale DNN models thanks to the unique spatial property of pattern-based sparsity and the help of the code generation capability of compilers.