  The rise of neural network (NN) applications has prompted an increased
interest in compression, with a particular focus on channel pruning, which does
not require any additional hardware. Most pruning methods employ either
single-layer operations or global schemes to determine which channels to remove
followed by fine-tuning of the network. In this paper we present Gator, a
channel-pruning method which temporarily adds learned gating mechanisms for
pruning of individual channels, and which is trained with an additional
auxiliary loss, aimed at reducing the computational cost due to memory,
(theoretical) speedup (in terms of FLOPs), and practical, hardware-specific
speedup. Gator introduces a new formulation of dependencies between NN layers
which, in contrast to most previous methods, enables pruning of non-sequential
parts, such as layers on ResNet's highway, and even removing entire ResNet
blocks. Gator's pruning for ResNet-50 trained on ImageNet produces
state-of-the-art (SOTA) results, such as 50% FLOPs reduction with only
0.4%-drop in top-5 accuracy. Also, Gator outperforms previous pruning models,
in terms of GPU latency by running 1.4 times faster. Furthermore, Gator
achieves improved top-5 accuracy results, compared to MobileNetV2 and
SqueezeNet, for similar runtimes. The source code of this work is available at:
https://github.com/EliPassov/gator.
