🎯 MDP: Multidimensional Vision Model Pruning with Latency Constraint

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This repository contains the official implementation of MDP method introduced in our CVPR 2025 paper:

MDP: Multidimensional Vision Model Pruning with Latency Constraint
Xinglong Sun, Barath Lakshmanan, Maying Shen, Shiyi Lan, Jingde Chen, Jose M. Alvarez

📋 Table of Contents

• License
• News
• Introduction
• Framework
• Results
• Installation
• Usage
• Acknowledgements
• Citation

📄 License

Please check the LICENSE file. HALP may be used non-commercially, meaning for research or evaluation purposes only. For business inquiries, please contact researchinquiries@nvidia.com.

📢 News

• [2025/09] Release license obtained. ResNet50 and ablation study code are now available; remaining code will be cleaned up and released soon.
• [2025/06] I presented MDP in a CVPR 2025 tutorial on Full-Stack, GPU-based Acceleration of Deep Learning and Foundation Models. You can watch the tutorial video here!

📝 Introduction

Current structural pruning methods face two significant limitations:

1. They often limit pruning to finer-grained levels like channels, making aggressive parameter reduction challenging
2. They focus heavily on parameter and FLOP reduction, with existing latency-aware methods frequently relying on simplistic, suboptimal linear models that fail to generalize well to transformers

In this paper, we address both limitations by introducing Multi-Dimensional Pruning (MDP), a novel paradigm that:

• Jointly optimizes across various pruning granularities (channels, query, key, heads, embeddings, and blocks)
• Employs advanced latency modeling to accurately capture latency variations
• Reformulates pruning as a Mixed-Integer Nonlinear Program (MINLP)
• Supports both CNNs and transformers

🎨 Framework

<div align="center"> <img src="Figs/framework.png" width="100%"> <p><i>Overview of our MDP method</i></p> </div>

📊 Results

Our extensive experiments demonstrate MDP's superior performance:

ImageNet Classification

• ResNet50: 28% speed increase with +1.4 Top-1 accuracy improvement over prior art
• DEIT-Base: 37% additional acceleration with +0.7 Top-1 accuracy improvement over prior art

3D Object Detection

• Higher speed (×1.18) and mAP (0.451 vs. 0.449) compared to dense baseline

<div align="center"> <img src="Figs/result.png" width="100%"> <p><i>MDP exhibits Pareto dominance with both CNNs and Transformers across various tasks</i></p> </div>

🚀 Installation

Please check README within the folder for the task you want to run!

💻 Usage

Please check README within the folder for the task you want to run!

🙏 Acknowledgements

Some of the infrastructure, data loading, and foundational code are adapted from HALP and NVIT works. We sincerely thank the authors of these works for their contributions.

📚 Citation

If you find this repository useful for your research, please cite our paper:

@misc{sun2025mdpmultidimensionalvisionmodel,
    title={MDP: Multidimensional Vision Model Pruning with Latency Constraint},
    author={Xinglong Sun and Barath Lakshmanan and Maying Shen and Shiyi Lan and Jingde Chen and Jose M. Alvarez},
    year={2025},
    eprint={2504.02168},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2504.02168}
}