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It is well known that multiplication operations in convolutional layers ofcommon CNNs consume a lot of time during inference stage. In this article wepresent a flexible method to decrease both computational complexity ofconvolutional layers in inference as well as amount of space to store them. Themethod is based on centroid filter quantization and outperforms approachesbased on tensor decomposition by a large margin. We performed comparativeanalysis of the proposed method and series of CP tensor decomposition onImageNet benchmark and found that our method provide almost 2.9 times bettercomputational gain. Despite the simplicity of our method it cannot be applieddirectly in inference stage in modern frameworks, but could be useful for casescalculation flow could be changed, e.g. for CNN-chip designers.

CNN inference acceleration using dictionary of centroids