Guaranteed Compression Rate for Activations in CNNs using a Frequency Pruning Approach
Sebastian Vogel1,a, Christoph Schorn1,b, Andre Guntoro1,c and Gerd Ascheid2
1Robert Bosch GmbH, Renningen, Germany
asebastian.vogel@de.bosch.com
bchristoph.schorn@de.bosch.com
candre.guntoro@de.bosch.com
2RWTH Aachen University, Aachen, Germany
gerd.ascheid@ice.rwth-aachen.de
ABSTRACT
Convolutional Neural Networks have become state of the art for many computer vision tasks. However, the size of Neural Networks prevents their application in resource constrained systems. In this work, we present a lossy compression technique for intermediate results of Convolutional Neural Networks. The proposed method offers guaranteed compression rates and additionally adapts to performance requirements. Our experiments with networks for classification and semantic segmentation show, that our method outperforms state-of-the-art compression techniques used in CNN accelerators.
Keywords: Deep Neural Networks, Convolutional Neural Networks, Compression, Embedded Systems.
