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The state-of-art DNN structures involve high computation and great demand formemory storage which pose intensive challenge on DNN framework resources. Tomitigate the challenges, weight pruning techniques has been studied. However,high accuracy solution for extreme structured pruning that combines differenttypes of structured sparsity still waiting for unraveling due to the extremelyreduced weights in DNN networks. In this paper, we propose a DNN frameworkwhich combines two different types of structured weight pruning (filter andcolumn prune) by incorporating alternating direction method of multipliers(ADMM) algorithm for better prune performance. We are the first to findnon-optimality of ADMM process and unused weights in a structured pruned model,and further design an optimization framework which contains the first proposedNetwork Purification and Unused Path Removal algorithms which are dedicated topost-processing an structured pruned model after ADMM steps. Some high lightsshows we achieve 232x compression on LeNet-5, 60x compression on ResNet-18CIFAR-10 and over 5x compression on AlexNet. We share our models at anonymouslink http://bit.ly/2VJ5ktv.

ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning