Deep Model Compression Via Two-Stage Deep Reinforcement Learning

TL;DR

This paper introduces a two-stage deep reinforcement learning approach for CNN model compression, combining pruning and quantization to significantly reduce model size while maintaining or improving accuracy.

Contribution

It proposes a novel DRL-based framework for jointly optimizing pruning and quantization in a two-stage pipeline for CNN compression.

Findings

Achieved 9x size reduction on CIFAR-10 with slight accuracy gain.

Reduced VGG-16 size by 33x on ImageNet with no accuracy loss.

Demonstrated effectiveness on CIFAR-10 and ImageNet datasets.

Abstract

Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For example, employing deep neural networks on mobile systems requires the design of accurate yet fast CNN for low latency in classification and object detection. To fulfill the need, we aim at obtaining CNN models with both high testing accuracy and small size to address resource constraints in many embedded devices. In particular, this paper focuses on proposing a generic reinforcement learning-based model compression approach in a two-stage compression pipeline: pruning and quantization. The first stage of compression, i.e., pruning, is achieved via exploiting deep reinforcement learning (DRL) to co-learn the accuracy and the FLOPs updated after layer-wise channel pruning and element-wise variational pruning via information dropout. The second stage, i.e., quantization, is achieved via a similar DRL approach but focuses on obtaining the optimal bits representation for individual layers. We further conduct experimental results on CIFAR-10 and ImageNet datasets. For the CIFAR-10 dataset, the proposed method can reduce the size of VGGNet by 9x from 20.04MB to 2.2MB with a slight accuracy increase. For the ImageNet dataset, the proposed method can reduce the size of VGG-16 by 33x from 138MB to 4.14MB with no accuracy loss.