Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction. (arXiv:2112.15445v1 [cs.LG])

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 …

arxiv deep learning gpu learning