  The proposed pruning strategy offers merits over weight-based pruning
techniques: (1) it avoids irregular memory access since representations and
matrices can be squeezed into their smaller but dense counterparts, leading to
greater speedup; (2) in a manner of top-down pruning, the proposed method
operates from a more global perspective based on training signals in the top
layer, and prunes each layer by propagating the effect of global signals
through layers, leading to better performances at the same sparsity level.
Extensive experiments show that at the same sparsity level, the proposed
strategy offers both greater speedup and higher performances than weight-based
pruning methods (e.g., magnitude pruning, movement pruning).
