  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 . A dedicated website offering a more
interactive comparison of structured pruning methods can be found at:
https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey .
