# Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey

arXiv: https://arxiv.org/abs/2005.04275 · HTML: https://mlsanity.com/paper/2005.04275/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 26 of 26)

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- [Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy](https://mlsanity.com/paper/2405.09014/) (2024)
- [Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices](https://mlsanity.com/paper/2512.08751/) (2025)
- [Neural Networks for Infectious Diseases Detection: Prospects and Challenges](https://mlsanity.com/paper/2112.03571/) (2021)
- [FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices](https://mlsanity.com/paper/2205.13272/) (2022)
- [Transferability of Winning Lottery Tickets in Neural Network Differential Equation Solvers](https://mlsanity.com/paper/2306.09863/) (2023)
- [Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimates](https://mlsanity.com/paper/2310.12461/) (2023)
- [Acceleration Algorithms in GNNs: A Survey](https://mlsanity.com/paper/2405.04114/) (2024)

## Abstract (arXiv)

With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices. In this paper, we provide a comprehensive survey on Pruning, a major compression strategy that removes non-critical or redundant neurons from a CNN model. The survey covers the overarching motivation for pruning, different strategies and criteria, their advantages and drawbacks, along with a compilation of major pruning techniques. We conclude the survey with a discussion on alternatives to pruning and current challenges for the model compression community.