Computer Science > Human-Computer Interaction
arXiv:2212.10774 (cs)
[Submitted on 21 Dec 2022]
Title:Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks
Authors:Rusheng Pan,Zhiyong Wang,Yating Wei,Han Gao,Gongchang Ou,Caleb Chen Cao,Jingli Xu,Tong Xu,Wei Chen
View a PDF of the paper titled Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks, by Rusheng Pan and 7 other authors
View PDFAbstract:A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2212.10774 [cs.HC] |
| (orarXiv:2212.10774v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2212.10774 arXiv-issued DOI via DataCite | |
| Journal reference: | IEEE Transactions on Visualization and Computer Graphics 1 (2022) 1-14 |
| Related DOI: | https://doi.org/10.1109/TVCG.2022.3230832 DOI(s) linking to related resources |
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View a PDF of the paper titled Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks, by Rusheng Pan and 7 other authors
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