# Accelerating Convolutional Neural Networks via Activation Map Compression

## TL;DR

This paper introduces a three-stage compression pipeline for CNN activation maps, significantly accelerating inference and reducing memory usage while improving accuracy on key vision benchmarks.

## Contribution

It proposes a novel sparsification, quantization, and entropy encoding method for activation maps, enhancing CNN efficiency and accuracy.

## Key findings

- Inception-V3 and MobileNet-V1 can be accelerated by up to 1.6x.
- Activation map compression achieves up to 6x reduction in memory.
- Accuracy increases of up to 0.55% on benchmark datasets.

## Abstract

The deep learning revolution brought us an extensive array of neural network architectures that achieve state-of-the-art performance in a wide variety of Computer Vision tasks including among others, classification, detection and segmentation. In parallel, we have also been observing an unprecedented demand in computational and memory requirements, rendering the efficient use of neural networks in low-powered devices virtually unattainable. Towards this end, we propose a three-stage compression and acceleration pipeline that sparsifies, quantizes and entropy encodes activation maps of Convolutional Neural Networks. Sparsification increases the representational power of activation maps leading to both acceleration of inference and higher model accuracy. Inception-V3 and MobileNet-V1 can be accelerated by as much as $1.6\times$ with an increase in accuracy of $0.38\%$ and $0.54\%$ on the ImageNet and CIFAR-10 datasets respectively. Quantizing and entropy coding the sparser activation maps lead to higher compression over the baseline, reducing the memory cost of the network execution. Inception-V3 and MobileNet-V1 activation maps, quantized to $16$ bits, are compressed by as much as $6\times$ with an increase in accuracy of $0.36\%$ and $0.55\%$ respectively.