Filter Pruning

Last Updated 25 April, 2026

by David Spuler, Ph.D.

What is Filter Pruning?

Filter pruning is a type of LLM inference optimization, primarily in relation to images, that reduces calculations along the width dimension of models. It is primarily related to CNNs, and is analogous to attention head pruning in Transformer architectures.

Filter Pruning: Book Excerpts and Blog Articles

Free online book excerpts with full text chapters online and free PDF downloads, and the Aussie AI blog, including related articles:

David Spuler, March 2024, Chapter 48. Width Pruning, in book "Generative AI in C++", https://www.aussieai.com/book/ch48-width-pruning
David Spuler, March 2024, Generative AI in C++: Coding Transformers and LLMs, https://www.aussieai.com/book/toc PDF: https://www.aussieai.com/pdf/BOOK-Generative-AI-CPP-Spuler-2024.pdf

Research on Filter Pruning

Research papers on filter pruning include:

M Sponner, B Waschneck, A Kumar , 2024, Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning, ACM Computing Surveys,, PDF: https://dl.acm.org/doi/pdf/10.1145/3657283 (Survey of various adaptive inference optimization techniques with much focus on image and video processing optimization for LLMs.)
You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang, Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo, Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin (Victor)Chan, Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia Jin, Guannan Zhang, Xiaodong Zeng, Nov 2023, On the Opportunities of Green Computing: A Survey, https://arxiv.org/abs/2311.00447 (Extensive survey of environmental and green AI issues, along with a survey of various optimization methods to reduce AI resource requirements in training and inference.)
Hanting Chen, Yunhe Wang, Han Shu, Yehui Tang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, and Chang Xu. 2020. Frequency Domain Compact 3D Convolutional Neural Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1641–1650. https://ieeexplore.ieee.org/document/9156798 https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.pdf
Y Hu, J Zhang, C Zhao, C Li, H Chen, 2023, Transformer Compression via Subspace Projection, arXiv preprint arXiv:2308.16475, https://arxiv.org/abs/2308.16475
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. 2017, Thinet: A filter level pruning method for deep neural network compression. In ICCV, pages 5058–5066, https://arxiv.org/abs/1707.06342
Zhepeng Wang, Isaacshubhanand Putla, Weiwen Jiang, Youzuo Lin, Oct 2023, Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices, https://arxiv.org/abs/2310.09667 (Using structured pruning via layerwise filter pruning to run a model on a Raspberry Pi.)
David Spuler, March 2024, Chapter 48. Width Pruning, Generative AI in C++: Coding Transformers and LLMs, https://www.amazon.com/dp/B0CXJKCWX9
Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, James Hensman, 9 Feb 2024 (v2), SliceGPT: Compress Large Language Models by Deleting Rows and Columns, Microsoft Research, https://arxiv.org/abs/2401.15024 Code: https://github.com/microsoft/TransformerCompression (Pruning of matrices effectively prunes along the width dimension and the "fourth" internal dimension of embeddings using techniques such as low-rank matrix factorization.)
Lampros Konstantinos, February 2024, Design and Implementation of a Filter Based Pruning Compression Technique for Convolution Neural Networks, Master's Thesis, Aristotle University of Thessalonikii, School of Informatics, Thessaloniki, Greece, https://ikee.lib.auth.gr/record/356738/files/GRI-2024-44302.pdf
Xiaotong Luo; Zekun Ai; Qiuyuan Liang; Yuan Xie, 06 August 2024, EdgeFormer: Edge-aware Efficient Transformer for Image Super-resolution, IEEE Transactions on Instrumentation and Measurement ( Early Access), DOI: 10.1109/TIM.2024.3436070, https://ieeexplore.ieee.org/abstract/document/10623619 https://github.com/xiaotongtt/EdgeFormer
Yang He, Lingao Xiao, 30 Nov 2023 (v2), Structured Pruning for Deep Convolutional Neural Networks: A survey, https://arxiv.org/abs/2303.00566 https://arxiv.org/pdf/2303.00566 https://ieeexplore.ieee.org/abstract/document/10330640 https://github.com/he-y/Awesome-Pruning https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey (Extensive survey of pruning for CNNs, not LLMs.)
Fabio Montello, Ronja Güldenring, Simone Scardapane, Lazaros Nalpantidis, 13 Jan 2025, A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion, https://arxiv.org/abs/2501.07451 (Survey of adaptive inference optimizations: early exit, dynamic routing, token skimming.)
Kuan-Ting Tu, Po-Hsien Yu, Yu-Syuan Tseng, Shao-Yi Chien, 30 Jul 2025, FGFP: A Fractional Gaussian Filter and Pruning for Deep Neural Networks Compression, https://arxiv.org/abs/2507.22527
David Spuler, March 2024, Chapter 48. Width Pruning, in book "Generative AI in C++", https://www.aussieai.com/book/ch48-width-pruning
David Spuler, March 2024, Generative AI in C++: Coding Transformers and LLMs, https://www.aussieai.com/book/toc PDF: https://www.aussieai.com/pdf/BOOK-Generative-AI-CPP-Spuler-2024.pdf

More Research on Pruning Types

Depth pruning (overview)
— Static layer pruning
— Layer pruning
— Early exit
— Dynamic layer pruning
— Layer skipping
— Layer approximation
— Shallow decoder architecture
— Layer reordering
— Layer Importance
Width pruning (overview)
— Attention head pruning
— Slimmable networks (width pruning)
— FFN pruning
— Channel pruning
— Filter pruning
Length pruning (longitudinal/input/end-to-end):
— Token pruning (input pruning)
— Dynamic token pruning
— Prompt compression
— Context compression
— Token merging
— Token skipping
— Token dropping
— Zero padding removal
Embedding-dimension pruning
— Embedding pruning
— Embedding matrix compression (embedding pruning)
— Embedding low-rank matrix factorization
— Unembedding matrix (output embeddings)
Multi-dimensional pruning
— Dual pruning
— Triple pruning
— Quadruple pruning
— 3D CNN model pruning