# Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression

arXiv: https://arxiv.org/abs/2509.04244 · HTML: https://mlsanity.com/paper/2509.04244/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [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)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://mlsanity.com/paper/1707.06342/) (2017)
- [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://mlsanity.com/paper/1811.00250/) (2018)
- [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://mlsanity.com/paper/1808.06866/) (2018)
- [Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights](https://mlsanity.com/paper/1702.03044/) (2017)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation](https://mlsanity.com/paper/2306.11222/) (2023)
- [Convolutional Neural Networks using Logarithmic Data Representation](https://mlsanity.com/paper/1603.01025/) (2016)
- [A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification](https://mlsanity.com/paper/2205.07877/) (2022)
- [QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models](https://mlsanity.com/paper/2310.08041/) (2023)
- [Differentiable Joint Pruning and Quantization for Hardware Efficiency](https://mlsanity.com/paper/2007.10463/) (2020)
- [Hessian-Aware Pruning and Optimal Neural Implant](https://mlsanity.com/paper/2101.08940/) (2021)
- [Searching for Low-Bit Weights in Quantized Neural Networks](https://mlsanity.com/paper/2009.08695/) (2020)
- [Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?](https://mlsanity.com/paper/1907.02124/) (2019)
- [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization](https://mlsanity.com/paper/2007.10026/) (2020)
- [Quantization without Tears](https://mlsanity.com/paper/2411.13918/) (2024)
- [Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks](https://mlsanity.com/paper/1808.05240/) (2018)
- [PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation](https://mlsanity.com/paper/2106.14681/) (2021)
- [Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization](https://mlsanity.com/paper/2312.15322/) (2023)
- [Progressive Local Filter Pruning for Image Retrieval Acceleration](https://mlsanity.com/paper/2001.08878/) (2020)
- [NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search](https://mlsanity.com/paper/2308.05600/) (2023)
- [TransAxx: Efficient Transformers with Approximate Computing](https://mlsanity.com/paper/2402.07545/) (2024)
- [Efficient CNNs via Passive Filter Pruning](https://mlsanity.com/paper/2304.02319/) (2023)
- [Concurrent Training and Layer Pruning of Deep Neural Networks](https://mlsanity.com/paper/2406.04549/) (2024)
- [Low-Rank Matrix Approximation for Neural Network Compression](https://mlsanity.com/paper/2504.20078/) (2025)
- [PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs](https://mlsanity.com/paper/2505.03254/) (2025)
- [Robustness-aware 2-bit quantization with real-time performance for neural network](https://mlsanity.com/paper/2010.11271/) (2020)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to the large number of layers and parameters, which result in considerable computational and memory demands. To address this issue, pruning and quantization are two widely used compression techniques, commonly applied individually in most studies to reduce model size and enhance processing speed. Nevertheless, combining these two techniques can yield even greater compression benefits. Effectively integrating pruning and quantization to harness their complementary advantages poses a challenging task, primarily due to their potential impact on model accuracy and the complexity of jointly optimizing both processes. In this paper, we propose two approaches that integrate similarity-based filter pruning with Adaptive Power-of-Two (APoT) quantization to achieve higher compression efficiency while preserving model accuracy. In the first approach, pruning and quantization are applied simultaneously during training. In the second approach, pruning is performed first to remove less important parameters, followed by quantization of the pruned model using low-bit representations. Experimental results demonstrate that our proposed approaches achieve effective model compression with minimal accuracy degradation, making them well-suited for deployment on devices with limited computational resources.