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BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
[article]
2020
arXiv
pre-print
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel computing architectures. However, both of the pruning dimensions and pruning methods lack universality, which leads to degraded performance and limited applicability. To solve the problem, we propose a new block-based pruning framework that
arXiv:2001.08357v2
fatcat:7nl364nd4bd6nozcmmvwg4rmdu