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.
|Title of host publication||Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings|
|Editors||Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||13|
|State||Published - 2021|
|Event||30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online|
Duration: 14 Sep 2021 → 17 Sep 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||30th International Conference on Artificial Neural Networks, ICANN 2021|
|Period||14/09/21 → 17/09/21|
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