# OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization

arXiv: https://arxiv.org/abs/2205.11141 · HTML: https://mlsanity.com/paper/2205.11141/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://mlsanity.com/paper/1707.06342/) (2017)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [ZeroQ: A Novel Zero Shot Quantization Framework](https://mlsanity.com/paper/2001.00281/) (2020)
- [Dynamic Network Surgery for Efficient DNNs](https://mlsanity.com/paper/1608.04493/) (2016)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [Data-free parameter pruning for Deep Neural Networks](https://mlsanity.com/paper/1507.06149/) (2015)
- [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy](https://mlsanity.com/paper/2006.08509/) (2020)
- [CARS: Continuous Evolution for Efficient Neural Architecture Search](https://mlsanity.com/paper/1909.04977/) (2019)
- [Deep Fried Convnets](https://mlsanity.com/paper/1412.7149/) (2014)
- [One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation](https://mlsanity.com/paper/1912.00120/) (2019)

## Built on by (most cited first, 19 of 19)

- [Pruning vs Quantization: Which is Better?](https://mlsanity.com/paper/2307.02973/) (2023)
- [STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs](https://mlsanity.com/paper/2408.01803/) (2024)
- [Effective Interplay between Sparsity and Quantization: From Theory to Practice](https://mlsanity.com/paper/2405.20935/) (2024)
- [Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression](https://mlsanity.com/paper/2502.16638/) (2025)
- [Channel-Wise Mixed-Precision Quantization for Large Language Models](https://mlsanity.com/paper/2410.13056/) (2024)
- [Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization](https://mlsanity.com/paper/2312.15322/) (2023)
- [Mixed-Precision Neural Networks: A Survey](https://mlsanity.com/paper/2208.06064/) (2022)
- [The Impact of Quantization and Pruning on Deep Reinforcement Learning Models](https://mlsanity.com/paper/2407.04803/) (2024)
- [Progressive Binarization with Semi-Structured Pruning for LLMs](https://mlsanity.com/paper/2502.01705/) (2025)
- [Towards Optimal Compression: Joint Pruning and Quantization](https://mlsanity.com/paper/2302.07612/) (2023)
- [Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression](https://mlsanity.com/paper/2603.18426/) (2026)
- [Differentiable Search for Finding Optimal Quantization Strategy](https://mlsanity.com/paper/2404.08010/) (2024)
- [Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing](https://mlsanity.com/paper/2505.16332/) (2025)
- [Group channel pruning and spatial attention distilling for object detection](https://mlsanity.com/paper/2306.01526/) (2023)
- [On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2411.00907/) (2024)
- [FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks](https://mlsanity.com/paper/2510.02822/) (2025)
- [Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture](https://mlsanity.com/paper/2509.12363/) (2025)
- [Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression](https://mlsanity.com/paper/2510.09696/) (2025)
- [Pruning Overparameterized Multi-Task Networks for Degraded Web Image Restoration](https://mlsanity.com/paper/2510.14463/) (2025)

## Abstract (arXiv)

As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e.g., pruning sparsity and quantization codebook) of each layer. Existing solutions obtain the compression allocation in an iterative/manual fashion while finetuning the compressed model, thus suffering from the efficiency issue. Different from the prior art, we propose a novel One-shot Pruning-Quantization (OPQ) in this paper, which analytically solves the compression allocation with pre-trained weight parameters only. During finetuning, the compression module is fixed and only weight parameters are updated. To our knowledge, OPQ is the first work that reveals pre-trained model is sufficient for solving pruning and quantization simultaneously, without any complex iterative/manual optimization at the finetuning stage. Furthermore, we propose a unified channel-wise quantization method that enforces all channels of each layer to share a common codebook, which leads to low bit-rate allocation without introducing extra overhead brought by traditional channel-wise quantization. Comprehensive experiments on ImageNet with AlexNet/MobileNet-V1/ResNet-50 show that our method improves accuracy and training efficiency while obtains significantly higher compression rates compared to the state-of-the-art.