The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at https://github.com/he-y/Awesome-Pruning

翻译：深度卷积神经网络（CNN）的卓越性能通常归因于其更深、更宽的网络架构，但这可能带来显著的计算成本。因此，剪枝神经网络因其能够有效降低存储和计算成本而受到关注。与产生非结构化模型的权重剪枝不同，结构化剪枝通过生成对硬件实现友好的模型，提供了实现实际加速的优势。结构化剪枝的特殊要求促使人们发现了许多新挑战并开发了创新解决方案。本文综述了深度CNN结构化剪枝的最新进展。我们总结并比较了最先进的结构化剪枝技术，涉及滤波器排序方法、正则化方法、动态执行、神经架构搜索、彩票假设以及剪枝的应用。在讨论结构化剪枝算法时，我们简要介绍了非结构化剪枝的对应方法，以强调它们之间的差异。此外，我们提供了结构化剪枝领域潜在研究机遇的见解。神经网络剪枝论文的精选列表可在https://github.com/he-y/Awesome-Pruning 找到。