In　recent　years,　deep　convolutional　neural　networks　(CNNs)　significantly　boost　the　various　applications,　but　the　high　computational　complexity　of　these　models　hinder　the　further　deployment　on　device　with　limited　computation　resources.　Hence,　dynamic　channel　pruning　has　been　recently　proposed　and　widely　used　for　compressing　CNN-based　models.　In　this　paper,　we　propose　a　novel　plug-and-play　dynamic　network　pruning　module.　With　very　slight　extra　computation　burden,　it　can　achieve　the　comparable　performance　as　the　original　model.　Specifically,　our　proposed　module　measures　the　importance　of　each　convolutional　channel　to　prune　the　CNNs　with　small　decrease　in　accuracy.　The　module　reduces　the　computation　cost　by　global　pooling　and　channel-wise　1-dimensional　convolution　that　considers　the　channels’　locality.　Comprehensive　experimental　results　demonstrate　the　effectiveness　of　our　module,　which　makes　a　better　trade-off　between　the　performance　and　the　acquired　computational　resources,　comparing　to　its　competing　methods.　In　concrete,　our　dynamic　pruning　module　can　reduce　51.1　%　FLOPs　of　VGG16　with　only　0.18　%　top-1　accuracy　degradation　on　CIFAR10.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.