Automatic Routability Predictor Development Using Neural Architecture Search Article Swipe
Chen-Chia Chang
,
Jingyu Pan
,
Tunhou Zhang
,
Zhiyao Xie
,
Jiang Hu
,
Weiyi Qi
,
Jerry Chun‐Wei Lin
,
Rongjian Liang
,
Joydeep Mitra
,
Elias Fallon
,
Yiran Chen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.01737
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.01737
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. However, manually crafted machine learning models require extensive human expertise and tremendous engineering efforts. In this work, we leverage neural architecture search (NAS) to automate the development of high-quality neural architectures for routability prediction, which can help to guide cell placement toward routable solutions. Our search method supports various operations and highly flexible connections, leading to architectures significantly different from all previous human-crafted models. Experimental results on a large dataset demonstrate that our automatically generated neural architectures clearly outperform multiple representative manually crafted solutions. Compared to the best case of manually crafted models, NAS-generated models achieve 5.85% higher Kendall's $τ$ in predicting the number of nets with DRC violations and 2.12% better area under ROC curve (ROC-AUC) in DRC hotspot detection. Moreover, compared with human-crafted models, which easily take weeks to develop, our efficient NAS approach finishes the whole automatic search process with only 0.3 days.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2012.01737
- https://arxiv.org/pdf/2012.01737
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308538784
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308538784Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2012.01737Digital Object Identifier
- Title
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Automatic Routability Predictor Development Using Neural Architecture SearchWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-03Full publication date if available
- Authors
-
Chen-Chia Chang, Jingyu Pan, Tunhou Zhang, Zhiyao Xie, Jiang Hu, Weiyi Qi, Jerry Chun‐Wei Lin, Rongjian Liang, Joydeep Mitra, Elias Fallon, Yiran ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2012.01737Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2012.01737Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2012.01737Direct OA link when available
- Concepts
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Leverage (statistics), Computer science, Machine learning, Electronic design automation, Artificial intelligence, Automation, Artificial neural network, Architecture, Boom, Data mining, Computer engineering, Engineering, Embedded system, Mechanical engineering, Art, Visual arts, Environmental engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.human | 35 |
| abstract_inverted_index.large | 95 |
| abstract_inverted_index.under | 140 |
| abstract_inverted_index.weeks | 156 |
| abstract_inverted_index.which | 61, 153 |
| abstract_inverted_index.whole | 165 |
| abstract_inverted_index.work, | 43 |
| abstract_inverted_index.better | 138 |
| abstract_inverted_index.degree | 21 |
| abstract_inverted_index.design | 14 |
| abstract_inverted_index.easily | 154 |
| abstract_inverted_index.higher | 124 |
| abstract_inverted_index.highly | 78 |
| abstract_inverted_index.method | 73 |
| abstract_inverted_index.models | 32, 121 |
| abstract_inverted_index.neural | 46, 56, 102 |
| abstract_inverted_index.number | 130 |
| abstract_inverted_index.search | 48, 72, 167 |
| abstract_inverted_index.toward | 68 |
| abstract_inverted_index.achieve | 122 |
| abstract_inverted_index.clearly | 104 |
| abstract_inverted_index.crafted | 29, 109, 118 |
| abstract_inverted_index.dataset | 96 |
| abstract_inverted_index.hotspot | 146 |
| abstract_inverted_index.improve | 19 |
| abstract_inverted_index.leading | 81 |
| abstract_inverted_index.machine | 3, 30 |
| abstract_inverted_index.models, | 119, 152 |
| abstract_inverted_index.models. | 90 |
| abstract_inverted_index.process | 168 |
| abstract_inverted_index.require | 33 |
| abstract_inverted_index.results | 92 |
| abstract_inverted_index.various | 75 |
| abstract_inverted_index.Compared | 111 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.approach | 162 |
| abstract_inverted_index.automate | 51 |
| abstract_inverted_index.compared | 149 |
| abstract_inverted_index.designs. | 26 |
| abstract_inverted_index.develop, | 158 |
| abstract_inverted_index.efforts. | 40 |
| abstract_inverted_index.finishes | 163 |
| abstract_inverted_index.flexible | 79 |
| abstract_inverted_index.inspires | 6 |
| abstract_inverted_index.learning | 4, 31 |
| abstract_inverted_index.leverage | 45 |
| abstract_inverted_index.manually | 28, 108, 117 |
| abstract_inverted_index.multiple | 106 |
| abstract_inverted_index.previous | 88 |
| abstract_inverted_index.routable | 69 |
| abstract_inverted_index.supports | 74 |
| abstract_inverted_index.(ROC-AUC) | 143 |
| abstract_inverted_index.Kendall's | 125 |
| abstract_inverted_index.Moreover, | 148 |
| abstract_inverted_index.automatic | 166 |
| abstract_inverted_index.different | 85 |
| abstract_inverted_index.efficient | 160 |
| abstract_inverted_index.expertise | 36 |
| abstract_inverted_index.extensive | 34 |
| abstract_inverted_index.generated | 101 |
| abstract_inverted_index.placement | 67 |
| abstract_inverted_index.automation | 15, 23 |
| abstract_inverted_index.detection. | 147 |
| abstract_inverted_index.electronic | 13 |
| abstract_inverted_index.operations | 76 |
| abstract_inverted_index.outperform | 105 |
| abstract_inverted_index.predicting | 128 |
| abstract_inverted_index.solutions. | 70, 110 |
| abstract_inverted_index.technology | 5 |
| abstract_inverted_index.tremendous | 38 |
| abstract_inverted_index.violations | 135 |
| abstract_inverted_index.demonstrate | 97 |
| abstract_inverted_index.development | 53 |
| abstract_inverted_index.engineering | 39 |
| abstract_inverted_index.prediction, | 60 |
| abstract_inverted_index.routability | 59 |
| abstract_inverted_index.Experimental | 91 |
| abstract_inverted_index.applications | 11 |
| abstract_inverted_index.architecture | 47 |
| abstract_inverted_index.connections, | 80 |
| abstract_inverted_index.high-quality | 55 |
| abstract_inverted_index.NAS-generated | 120 |
| abstract_inverted_index.architectures | 57, 83, 103 |
| abstract_inverted_index.automatically | 100 |
| abstract_inverted_index.human-crafted | 89, 151 |
| abstract_inverted_index.significantly | 84 |
| abstract_inverted_index.representative | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |