Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data Article Swipe
Jingyu Pan
,
Chen-Chia Chang
,
Zhiyao Xie
,
Ang Li
,
Minxue Tang
,
Tunhou Zhang
,
Jiang Hu
,
Yiran Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.16009
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.16009
Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.16009
- https://arxiv.org/pdf/2203.16009
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221165938
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4221165938Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2203.16009Digital Object Identifier
- Title
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Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private DataWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-30Full publication date if available
- Authors
-
Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, Yiran ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.16009Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2203.16009Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2203.16009Direct OA link when available
- Concepts
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Computer science, Personalization, Estimator, Quality (philosophy), Constraint (computer-aided design), Machine learning, Artificial intelligence, Engineering, Epistemology, Mechanical engineering, Statistics, Philosophy, Mathematics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.trained | 141 |
| abstract_inverted_index.various | 14 |
| abstract_inverted_index.without | 148 |
| abstract_inverted_index.Although | 73 |
| abstract_inverted_index.Applying | 0 |
| abstract_inverted_index.academic | 31 |
| abstract_inverted_index.accuracy | 189 |
| abstract_inverted_index.approach | 125, 133 |
| abstract_inverted_index.becoming | 105 |
| abstract_inverted_index.company, | 83 |
| abstract_inverted_index.compared | 192 |
| abstract_inverted_index.explicit | 149 |
| abstract_inverted_index.improves | 188 |
| abstract_inverted_index.learning | 2 |
| abstract_inverted_index.limiting | 107 |
| abstract_inverted_index.multiple | 145 |
| abstract_inverted_index.previous | 207 |
| abstract_inverted_index.privacy. | 158 |
| abstract_inverted_index.promise, | 24 |
| abstract_inverted_index.reality, | 52 |
| abstract_inverted_index.results, | 163 |
| abstract_inverted_index.training | 49, 79, 177, 187, 213 |
| abstract_inverted_index.co-design | 165 |
| abstract_inverted_index.companies | 69 |
| abstract_inverted_index.scenario. | 178 |
| abstract_inverted_index.commission | 76 |
| abstract_inverted_index.companies. | 99 |
| abstract_inverted_index.constraint | 108 |
| abstract_inverted_index.customized | 167, 199 |
| abstract_inverted_index.developers | 54 |
| abstract_inverted_index.especially | 96 |
| abstract_inverted_index.estimators | 209 |
| abstract_inverted_index.inadequate | 93 |
| abstract_inverted_index.individual | 194 |
| abstract_inverted_index.industrial | 34 |
| abstract_inverted_index.researches | 32 |
| abstract_inverted_index.respecting | 155 |
| abstract_inverted_index.strengthen | 161 |
| abstract_inverted_index.Experiments | 179 |
| abstract_inverted_index.outperforms | 203 |
| abstract_inverted_index.predictions | 19 |
| abstract_inverted_index.routability | 208 |
| abstract_inverted_index.applications | 15, 129 |
| abstract_inverted_index.availability | 42, 102 |
| abstract_inverted_index.demonstrated | 28 |
| abstract_inverted_index.high-quality | 48 |
| abstract_inverted_index.well-studied | 127 |
| abstract_inverted_index.collaborative | 186, 212 |
| abstract_inverted_index.comprehensive | 182 |
| abstract_inverted_index.confidential. | 72 |
| abstract_inverted_index.decentralized | 176 |
| abstract_inverted_index.effectiveness | 37 |
| abstract_inverted_index.significantly | 202 |
| abstract_inverted_index.optimizations. | 21 |
| abstract_inverted_index.collaboratively | 140 |
| abstract_inverted_index.personalization | 173 |
| abstract_inverted_index.Federated-Learning | 123 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |