  It is well known that multiplication operations in convolutional layers of
common CNNs consume a lot of time during inference stage. In this article we
present a flexible method to decrease both computational complexity of
convolutional layers in inference as well as amount of space to store them. The
method is based on centroid filter quantization and outperforms approaches
based on tensor decomposition by a large margin. We performed comparative
analysis of the proposed method and series of CP tensor decomposition on
ImageNet benchmark and found that our method provide almost 2.9 times better
computational gain. Despite the simplicity of our method it cannot be applied
directly in inference stage in modern frameworks, but could be useful for cases
calculation flow could be changed, e.g. for CNN-chip designers.
