  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 DNN topologies 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 model compression methods. We evaluated our method on
various models from typical to mobile-friendly networks, such as ResNet family,
VGG-16, MobileNet-v1/v2, and ShuffleNet. Results show that our method can
achieve higher compression ratios with a minimal fine-tuning cost yet yields
outstanding and competitive performance.
