RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration

TL;DR

RL-Pruner employs reinforcement learning to automatically determine optimal structured pruning strategies for CNNs, effectively reducing model size and inference time while maintaining accuracy across various architectures.

Contribution

It introduces a reinforcement learning-based method to learn optimal pruning distributions across layers, improving CNN compression without model-specific adjustments.

Findings

Effective in compressing GoogleNet, ResNet, MobileNet

Maintains accuracy while reducing model size

Outperforms other structured pruning methods

Abstract

Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in recent years. Compressing these models not only reduces storage requirements, making deployment to edge devices feasible, but also accelerates inference, thereby reducing latency and computational costs. Structured pruning, which removes filters at the layer level, directly modifies the model architecture. This approach achieves a more compact architecture while maintaining target accuracy, ensuring that the compressed model retains good compatibility and hardware efficiency. Our method is based on a key observation: filters in different layers of a neural network have varying importance to the model's performance. When the number of filters to prune is fixed, the optimal pruning distribution across different layers is uneven to minimize performance loss. Layers that are more sensitive to pruning should account for a smaller proportion of the pruning distribution. To leverage this insight, we propose RL-Pruner, which uses reinforcement learning to learn the optimal pruning distribution. RL-Pruner can automatically extract dependencies between filters in the input model and perform pruning, without requiring model-specific pruning implementations. We conducted experiments on models such as GoogleNet, ResNet, and MobileNet, comparing our approach to other structured pruning methods to validate its effectiveness. Our code is available at https://github.com/Beryex/RLPruner-CNN.