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arXiv · 2005.04275

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

Jiayi Liu·Samarth Tripathi·Unmesh Kurup·Mohak Shah
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Abstract

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.

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Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah. 2020-05-08. Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey. https://arxiv.org/abs/2005.04275

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