- 专利标题: AUTOMATIC CHANNEL PRUNING VIA GRAPH NEURAL NETWORK BASED HYPERNETWORK
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申请号: US17846555申请日: 2022-06-22
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公开(公告)号: US20230084203A1公开(公告)日: 2023-03-16
- 发明人: Baopu LI , Qiuling SUO , Yuchen BIAN
- 申请人: Baidu USA, LLC
- 申请人地址: US CA Sunnyvale
- 专利权人: Baidu USA, LLC
- 当前专利权人: Baidu USA, LLC
- 当前专利权人地址: US CA Sunnyvale
- 主分类号: G06N3/08
- IPC分类号: G06N3/08 ; G06N3/04
摘要:
Model pruning is used to trim large neural networks, like convolutional neural networks (CNNs), to reduce computation overheads. Existing model pruning methods mainly rely on heuristics rules or local relationships of CNN layers. A novel hypernetwork based on graph neural network is disclosed for generating and evaluating pruned networks. A graph is first constructed according to information flow of channels and layers in a CNN network, with channels and layers represented as nodes and information flows represented as edges. A graph neural network is applied to aggregate both local and global dependencies across all channels and layers of the CNN network, resulting in informative node embeddings. With such embeddings, pruned CNN networks including their architectures and weights may be effectively generated and evaluated.