Model compression is an essential technique for deploying deep neural networks (DNNs) on power and memory-constrained resources. However, existing model-compression methods often rely on human expertise and focus on parameters' local importance, ignoring the rich topology information within DNNs. In this paper, we propose a novel multi-stage graph embedding technique based on graph neural networks (GNNs) to identify the DNNs' topology and use reinforcement learning (RL) to find a suitable compression policy. We performed resource-constrained (i.e., FLOPs) channel pruning and compared our approach with state-of-the-art compression methods using over-parameterized DNNs (e.g., ResNet and VGG-16) and mobile-friendly DNNs (e.g., MobileNet and ShuffleNet). We evaluated our method on various models from typical to mobile-friendly networks, such as ResNet family, VGG-16, MobileNet-v1/v2, and ShuffleNet. The results demonstrate that our method can prune dense networks (e.g., VGG-16) by up to 80% of their original FLOPs. More importantly, our method outperformed state-of-the-art methods and achieved a higher accuracy by up to 1.84% for ShuffleNet-v1. Furthermore, following our approach, the pruned VGG-16 achieved a noticeable 1.38$\times$ speed up and 141 MB GPU memory reduction.

本篇文章提出了一种基于图神经网络和强化学习的多阶段图嵌入技术，用于识别DNN的拓扑结构和寻找合适的压缩策略，实现模型压缩并获得更高的压缩比和竞争性的性能。

利用多阶段图嵌入和强化学习的拓扑感知网络剪枝