  This paper presents a survey of methods for pruning deep neural networks. It
begins by categorising over 150 studies based on the underlying approach used
and then focuses on three categories: methods that use magnitude based pruning,
methods that utilise clustering to identify redundancy, and methods that use
sensitivity analysis to assess the effect of pruning. Some of the key
influencing studies within these categories are presented to highlight the
underlying approaches and results achieved. Most studies present results which
are distributed in the literature as new architectures, algorithms and data
sets have developed with time, making comparison across different studied
difficult. The paper therefore provides a resource for the community that can
be used to quickly compare the results from many different methods on a variety
of data sets, and a range of architectures, including AlexNet, ResNet, DenseNet
and VGG. The resource is illustrated by comparing the results published for
pruning AlexNet and ResNet50 on ImageNet and ResNet56 and VGG16 on the CIFAR10
data to reveal which pruning methods work well in terms of retaining accuracy
whilst achieving good compression rates. The paper concludes by identifying
some promising directions for future research.
