论文标题
修剪算法以加速卷积神经网络以进行边缘应用：一项调查
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey
论文作者
论文摘要
随着增加卷积神经网络（CNN）模型大小的一般趋势，模型压缩和加速技术对于在边缘设备上部署这些模型变得至关重要。在本文中，我们提供了有关修剪的全面调查，该调查是一种主要的压缩策略，可去除CNN模型中的非关键或冗余神经元。该调查涵盖了修剪，不同策略和标准的总体动机，其优势和缺点，以及主要修剪技术的汇编。我们通过讨论对模型压缩社区的修剪和当前挑战的替代方案进行了讨论。
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.
