# Structured Compression by Weight Encryption for Unstructured Pruning and Quantization

arXiv: https://arxiv.org/abs/1905.10138 · HTML: https://mlsanity.com/paper/1905.10138/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 16 of 16)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Hessian-Aware Pruning and Optimal Neural Implant](https://mlsanity.com/paper/2101.08940/) (2021)
- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
- [Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization](https://mlsanity.com/paper/2312.15322/) (2023)
- [Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup](https://mlsanity.com/paper/2312.05795/) (2023)
- [An Effective Information Theoretic Framework for Channel Pruning](https://mlsanity.com/paper/2408.16772/) (2024)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [ONNXPruner: ONNX-Based General Model Pruning Adapter](https://mlsanity.com/paper/2404.08016/) (2024)
- [Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach](https://mlsanity.com/paper/2302.10483/) (2023)
- [Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression](https://mlsanity.com/paper/2105.01869/) (2021)
- [Online Robot Navigation and Manipulation with Distilled Vision-Language Models](https://mlsanity.com/paper/2401.17083/) (2024)
- [Bayesian Federated Model Compression for Communication and Computation Efficiency](https://mlsanity.com/paper/2404.07532/) (2024)
- [Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values](https://mlsanity.com/paper/2110.04492/) (2021)
- [RanDeS: Randomized Delta Superposition for Multi-Model Compression](https://mlsanity.com/paper/2505.11204/) (2025)
- [UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs](https://mlsanity.com/paper/2507.07885/) (2025)

## Abstract (arXiv)

Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however, still challenging mainly due to the irregular representations of sparse matrix formats. This paper proposes a new weight representation scheme for Sparse Quantized Neural Networks, specifically achieved by fine-grained and unstructured pruning method. The representation is encrypted in a structured regular format, which can be efficiently decoded through XOR-gate network during inference in a parallel manner. We demonstrate various deep learning models that can be compressed and represented by our proposed format with fixed and high compression ratio. For example, for fully-connected layers of AlexNet on ImageNet dataset, we can represent the sparse weights by only 0.28 bits/weight for 1-bit quantization and 91% pruning rate with a fixed decoding rate and full memory bandwidth usage. Decoding through XOR-gate network can be performed without any model accuracy degradation with additional patch data associated with small overhead.