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

Layer-wise Model Pruning based on Mutual Information