Originally published at: https://developer.nvidia.com/blog/sparsity-in-int8-training-workflow-and-best-practices-for-tensorrt-acceleration/
The training stage of deep learning (DL) models consists of learning numerous dense floating-point weight matrices, which results in a massive amount of floating-point computations during inference. Research has shown that many of those computations can be skipped by forcing some weights to be zero, with little impact on the final accuracy. In parallel to…