# Differentiable Joint Pruning and Quantization for Hardware Efficiency

arXiv: https://arxiv.org/abs/2007.10463 · HTML: https://mlsanity.com/paper/2007.10463/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 30 of 46)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [PTQD: Accurate Post-Training Quantization for Diffusion Models](https://mlsanity.com/paper/2305.10657/) (2023)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [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)
- [Hessian-Aware Pruning and Optimal Neural Implant](https://mlsanity.com/paper/2101.08940/) (2021)
- [Differentiable Model Compression via Pseudo Quantization Noise](https://mlsanity.com/paper/2104.09987/) (2021)
- [NIPQ: Noise proxy-based Integrated Pseudo-Quantization](https://mlsanity.com/paper/2206.00820/) (2022)
- [Sharpness-aware Quantization for Deep Neural Networks](https://mlsanity.com/paper/2111.12273/) (2021)
- [Wavelet Feature Maps Compression for Image-to-Image CNNs](https://mlsanity.com/paper/2205.12268/) (2022)
- [Efficient Quantization Strategies for Latent Diffusion Models](https://mlsanity.com/paper/2312.05431/) (2023)
- [Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression](https://mlsanity.com/paper/2502.16638/) (2025)
- [Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching](https://mlsanity.com/paper/2301.00131/) (2022)
- [PDP: Parameter-free Differentiable Pruning is All You Need](https://mlsanity.com/paper/2305.11203/) (2023)
- [Generalizable Mixed-Precision Quantization via Attribution Rank Preservation](https://mlsanity.com/paper/2108.02720/) (2021)
- [Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization](https://mlsanity.com/paper/2312.15322/) (2023)
- [Minimum Variance Unbiased N:M Sparsity for the Neural Gradients](https://mlsanity.com/paper/2203.10991/) (2022)
- [MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization](https://mlsanity.com/paper/2311.06798/) (2023)
- [Single-path Bit Sharing for Automatic Loss-aware Model Compression](https://mlsanity.com/paper/2101.04935/) (2021)
- [FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation](https://mlsanity.com/paper/2102.07444/) (2021)
- [Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks](https://mlsanity.com/paper/2407.01054/) (2024)
- [Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs](https://mlsanity.com/paper/2509.11177/) (2025)
- [Towards Hardware-Specific Automatic Compression of Neural Networks](https://mlsanity.com/paper/2212.07818/) (2022)
- [Towards Optimal Compression: Joint Pruning and Quantization](https://mlsanity.com/paper/2302.07612/) (2023)
- [Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis](https://mlsanity.com/paper/2504.13296/) (2025)
- [Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding](https://mlsanity.com/paper/2206.15014/) (2022)
- [LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch](https://mlsanity.com/paper/2309.14157/) (2023)
- [Differentiable Search for Finding Optimal Quantization Strategy](https://mlsanity.com/paper/2404.08010/) (2024)
- [CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks](https://mlsanity.com/paper/2512.12981/) (2025)
- [Learning Accurate Performance Predictors for Ultrafast Automated Model Compression](https://mlsanity.com/paper/2304.06393/) (2023)

## Abstract (arXiv)

We present a differentiable joint pruning and quantization (DJPQ) scheme. We frame neural network compression as a joint gradient-based optimization problem, trading off between model pruning and quantization automatically for hardware efficiency. DJPQ incorporates variational information bottleneck based structured pruning and mixed-bit precision quantization into a single differentiable loss function. In contrast to previous works which consider pruning and quantization separately, our method enables users to find the optimal trade-off between both in a single training procedure. To utilize the method for more efficient hardware inference, we extend DJPQ to integrate structured pruning with power-of-two bit-restricted quantization. We show that DJPQ significantly reduces the number of Bit-Operations (BOPs) for several networks while maintaining the top-1 accuracy of original floating-point models (e.g., 53x BOPs reduction in ResNet18 on ImageNet, 43x in MobileNetV2). Compared to the conventional two-stage approach, which optimizes pruning and quantization independently, our scheme outperforms in terms of both accuracy and BOPs. Even when considering bit-restricted quantization, DJPQ achieves larger compression ratios and better accuracy than the two-stage approach.