  This work is focused on the pruning of some convolutional neural networks
(CNNs) and improving theirs efficiency on graphic processing units (GPU) by
using a direct sparse algorithm. The Nvidia deep neural network (cuDnn) library
is the most effective implementations of deep learning (DL) algorithms for
GPUs. GPUs are the most commonly used accelerators for deep learning
computations. One of the most common techniques for improving the efficiency of
CNN models is weight pruning and quantization. There are two main types of
pruning: structural and non-structural. The first enables much easier
acceleration on many type of accelerators, but with this type it is difficult
to achieve a sparsity level and accuracy as high as that obtained with the
second type. Non-structural pruning with retraining can generate a weight
tensors up to 90% or more of sparsity in some deep CNN models. In this article
the pruning algorithm is presented which makes it possible to achieve high
sparsity levels without accuracy drop. In the next stage the linear and
non-linear quantization is adapted for further time and footprint reduction.
This paper is an extended of previously published paper concerning effective
pruning techniques and present real models pruned with high sparsities and
reduced precision which can achieve better performance than the CuDnn library.
