# Structured Probabilistic Pruning for Convolutional Neural Network Acceleration

Source: [Structured Probabilistic Pruning for Convolutional Neural Network Acceleration](<https://www.sota2.com/research/paper/68fa64e01e209131cea06ff9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:13:58.604Z

Authors: Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu

Published: 2017-09-20T03:13:40.000Z

## Abstract

In this paper, we propose a novel progressive parameter pruning method for Convolutional Neural Network acceleration, named Structured Probabilistic Pruning (SPP), which effectively prunes weights of convolutional layers in a probabilistic manner. Unlike existing deterministic pruning approaches, where unimportant weights are permanently eliminated, SPP introduces a pruning probability for each weight, and pruning is guided by sampling from the pruning probabilities. A mechanism is designed to increase and decrease pruning probabilities based on importance criteria in the training process. Experiments show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet classification. Moreover, SPP can be directly applied to accelerate multi-branch CNN networks, such as ResNet, without specific adaptations. Our 2x speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet. We further show the effectiveness of SPP on transfer learning tasks.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1709.06994>)

[PDF](<https://arxiv.org/pdf/1709.06994.pdf>)