DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Understand

Neural network pruning is one of the most popular methods of accelerating the inference of deep convolutional neural networks (CNNs).

• The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time.
• However, we argue that they are not equivalent if parallel computing is considered.
• Given that filter-level pruning only prunes filters in layers and computations in a layer usually run in parallel, most computations reduced by filter-level pruning usually run in parallel with the un-reduced ones.