  Deep Learning models have become the dominant approach in several areas due
to their high performance. Unfortunately, the size and hence computational
requirements of operating such models can be considerably high. Therefore, this
constitutes a limitation for deployment on memory and battery constrained
devices such as mobile phones or embedded systems. To address these
limitations, we propose a novel and simple pruning method that compresses
neural networks by removing entire filters and neurons according to a global
threshold across the network without any pre-calculation of layer sensitivity.
The resulting model is compact, non-sparse, with the same accuracy as the
non-compressed model, and most importantly requires no special infrastructure
for deployment. We prove the viability of our method by producing highly
compressed models, namely VGG-16, ResNet-56, and ResNet-110 respectively on
CIFAR10 without losing any performance compared to the baseline, as well as
ResNet-34 and ResNet-50 on ImageNet without a significant loss of accuracy. We
also provide a well-retrained 30% compressed ResNet-50 that slightly surpasses
the base model accuracy. Additionally, compressing more than 56% and 97% of
AlexNet and LeNet-5 respectively. Interestingly, the resulted models' pruning
patterns are highly similar to the other methods using layer sensitivity
pre-calculation step. Our method does not only exhibit good performance but
what is more also easy to implement.
