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The rise of neural network (NN) applications has prompted an increasedinterest in compression, with a particular focus on channel pruning, which doesnot require any additional hardware. Most pruning methods employ eithersingle-layer operations or global schemes to determine which channels to removefollowed by fine-tuning of the network. In this paper we present Gator, achannel-pruning method which temporarily adds learned gating mechanisms forpruning of individual channels, and which is trained with an additionalauxiliary loss, aimed at reducing the computational cost due to memory,(theoretical) speedup (in terms of FLOPs), and practical, hardware-specificspeedup. Gator introduces a new formulation of dependencies between NN layerswhich, in contrast to most previous methods, enables pruning of non-sequentialparts, such as layers on ResNet's highway, and even removing entire ResNetblocks. Gator's pruning for ResNet-50 trained on ImageNet producesstate-of-the-art (SOTA) results, such as 50% FLOPs reduction with only0.4%-drop in top-5 accuracy. Also, Gator outperforms previous pruning models,in terms of GPU latency by running 1.4 times faster. Furthermore, Gatorachieves improved top-5 accuracy results, compared to MobileNetV2 andSqueezeNet, for similar runtimes. The source code of this work is available at:https://github.com/EliPassov/gator.

Gator: Customizable Channel Pruning of Neural Networks with Gating