CNN inference acceleration using dictionary of centroids

TL;DR

This paper introduces a centroid filter quantization method to accelerate CNN inference by reducing computational complexity and storage, outperforming tensor decomposition approaches on ImageNet.

Contribution

The paper presents a novel centroid filter quantization technique that significantly improves CNN inference speed and efficiency compared to existing tensor decomposition methods.

Findings

Achieved 2.9x computational gain over CP tensor decomposition.

Method outperforms tensor decomposition in efficiency on ImageNet.

Potential applications in CNN-chip design and custom inference pipelines.

Abstract

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.