We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in lowpower devices such as mobile phones. Inspired by the loop perforation technique from source code optimization, we speed up the bottleneck convolutional layers by skipping their evaluation in some of the spatial positions. We propose and analyze several strategies of choosing these positions. We demonstrate that perforation can accelerate modern convolutional networks such as AlexNet and VGG-16 by a factor of 2× -4×. Additionally, we show that perforation is complementary to the recently proposed acceleration method of Zhang et al. [28] .
Figurnov et al. REF speeded up the convolutional layers by skipping operations in some spatial positions, which is based on loop perforation from source code optimization.
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
ArXiv
ArXiv
Computer Science