Retrospective: Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism

Back in 2017, Deep Neural Networks (DNN) models were fundamentally changing the development of artificial intelligence applications, and the high redundancy inside these larger DNN models, many researchers proposed pruning to remove unnecessary parameters, thereby reducing both the corresponding computation and memory footprints.