Auto Graph Encoder-Decoder for Model Compression and Network Acceleration

Model compression aims to deploy deep neural networks (DNN) to mobile devices with limited computing power and storage resource. However, most of the existing model compression methods rely on manually defined rules, which requires domain expertise. In this paper, we propose an Auto Graph encoder-decoder Model Compression (AGMC) method combined with graph neural networks (GNN) and reinforcement learning (RL) to find the best compression policy. We model the target DNN as a graph and use GNN to learn the embeddings of the DNN automatically. In our experiments, we first compared our method with rule-based DNN embedding methods to show the graph auto encoder-decoder's effectiveness. Our learning-based DNN embedding achieved better performance and a higher compression ratio with fewer search steps. Moreover, we evaluated the AGMC on CIFAR-10 and ILSVRC-2012 datasets and compared handcrafted and learning-based model compression approaches. Our method outperformed handcrafted and learning-based methods on ResNet-56 with 3.6% and 1.8% higher accuracy, respectively. Furthermore, we achieved a higher compression ratio than state-of-the-art methods on MobileNet-V2 with just 0.93% accuracy loss.