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Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
[article]
2022
arXiv
pre-print
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
arXiv:2102.03214v2
fatcat:v63nnyzm65b4ln4eiwteqgpdzu