  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.
