  Weight pruning is a technique to make Deep Neural Network (DNN) inference
more computationally efficient by reducing the number of model parameters over
the course of training. However, most weight pruning techniques generally does
not speed up DNN training and can even require more iterations to reach model
convergence. In this work, we propose a novel Structured Data Gradient Pruning
(SDGP) method that can speed up training without impacting model convergence.
This approach enforces a specific sparsity structure, where only N out of every
M elements in a matrix can be nonzero, making it amenable to hardware
acceleration. Modern accelerators such as the Nvidia A100 GPU support this type
of structured sparsity for 2 nonzeros per 4 elements in a reduction. Assuming
hardware support for 2:4 sparsity, our approach can achieve a 15-25\% reduction
in total training time without significant impact to performance. Source code
and pre-trained models are available at
\url{https://github.com/BradMcDanel/sdgp}.
