Applying GNNs to Timing Estimation at RTL : (Invited Paper)
In the Electronic Design Automation (EDA) flow, signoff checks, such as timing analysis, are performed only after physical synthesis. Encountered timing violations cause re-iterations of the design flow. Hence, timing estimations at initial design stages, such as Register Transfer Level (RTL), would...
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| Published in: | 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) pp. 1 - 8 |
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| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
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ACM
29.10.2022
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| ISSN: | 1558-2434 |
| Online Access: | Get full text |
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| Abstract | In the Electronic Design Automation (EDA) flow, signoff checks, such as timing analysis, are performed only after physical synthesis. Encountered timing violations cause re-iterations of the design flow. Hence, timing estimations at initial design stages, such as Register Transfer Level (RTL), would increase the quality of the results and lower the flow iterations. Machine learning has been used to estimate the timing behavior of chip components. However, existing solutions map EDA objects to Euclidean data without considering that EDA objects are represented naturally as graphs. Recent advances in Graph Neural Networks (GNNs) motivate the mapping from EDA objects to graphs for design metric prediction tasks at different stages. This paper maps RTL designs to directed, featured graphs with multidimensional node and edge features. These are the input to GNNs for estimating component delays and slews. An in-house hardware generation framework and open-source EDA tools for ASIC synthesis are employed for collecting training data. Experiments over unseen circuits show that GNN-based models are promising for timing estimation, even when the features come from early RTL implementations. Based on estimated delays, critical areas of the design can be detected, and proper RTL micro-architectures can be chosen without running long design iterations. |
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| AbstractList | In the Electronic Design Automation (EDA) flow, signoff checks, such as timing analysis, are performed only after physical synthesis. Encountered timing violations cause re-iterations of the design flow. Hence, timing estimations at initial design stages, such as Register Transfer Level (RTL), would increase the quality of the results and lower the flow iterations. Machine learning has been used to estimate the timing behavior of chip components. However, existing solutions map EDA objects to Euclidean data without considering that EDA objects are represented naturally as graphs. Recent advances in Graph Neural Networks (GNNs) motivate the mapping from EDA objects to graphs for design metric prediction tasks at different stages. This paper maps RTL designs to directed, featured graphs with multidimensional node and edge features. These are the input to GNNs for estimating component delays and slews. An in-house hardware generation framework and open-source EDA tools for ASIC synthesis are employed for collecting training data. Experiments over unseen circuits show that GNN-based models are promising for timing estimation, even when the features come from early RTL implementations. Based on estimated delays, critical areas of the design can be detected, and proper RTL micro-architectures can be chosen without running long design iterations. |
| Author | Ecker, Wolfgang Lopera, Daniela Sanchez |
| Author_xml | – sequence: 1 givenname: Daniela Sanchez surname: Lopera fullname: Lopera, Daniela Sanchez email: daniela.sanchezlopera@infineon.com organization: Technical University of Munich,Infineon Technologies AG,Munich,Germany – sequence: 2 givenname: Wolfgang surname: Ecker fullname: Ecker, Wolfgang email: wolfgang.ecker@infineon.com organization: Technical University of Munich,Infineon Technologies AG,Munich,Germany |
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| Snippet | In the Electronic Design Automation (EDA) flow, signoff checks, such as timing analysis, are performed only after physical synthesis. Encountered timing... |
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| SubjectTerms | Computer architecture Delay Delays Design automation Electronic Design Automation Estimation Graph Neural Networks Measurement Register Transfer Level Slew Timing Analysis Topology Training data |
| Title | Applying GNNs to Timing Estimation at RTL : (Invited Paper) |
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