RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning
Concurrent Clock and Data (CCD) optimization is a well-adopted approach in modern commercial tools that resolves timing violations using a mixture of clock skewing and delay fixing strategies. However, existing CCD algorithms are flawed. Particularly, they fail to prioritize violating endpoints for...
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| Vydáno v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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09.07.2023
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| Abstract | Concurrent Clock and Data (CCD) optimization is a well-adopted approach in modern commercial tools that resolves timing violations using a mixture of clock skewing and delay fixing strategies. However, existing CCD algorithms are flawed. Particularly, they fail to prioritize violating endpoints for different optimization strategies correctly, leading to flow-wise globally sub-optimal results. In this paper, we overcome this issue by presenting RL-CCD, a Reinforcement Learning (RL) agent that selects endpoints for useful skew prioritization using the proposed EP-GNN, an endpoint-oriented Graph Neural Network (GNN) model, and a Transformer-based self-supervised attention mechanism. Experimental results on 19 industrial designs in 5 − 12nm technologies demonstrate that RL-CCD achieves up to 64% Total Negative Slack (TNS) reduction and 66.5% number of violating endpoints (NVE) improvement over the native implementation of a commercial tool. |
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| AbstractList | Concurrent Clock and Data (CCD) optimization is a well-adopted approach in modern commercial tools that resolves timing violations using a mixture of clock skewing and delay fixing strategies. However, existing CCD algorithms are flawed. Particularly, they fail to prioritize violating endpoints for different optimization strategies correctly, leading to flow-wise globally sub-optimal results. In this paper, we overcome this issue by presenting RL-CCD, a Reinforcement Learning (RL) agent that selects endpoints for useful skew prioritization using the proposed EP-GNN, an endpoint-oriented Graph Neural Network (GNN) model, and a Transformer-based self-supervised attention mechanism. Experimental results on 19 industrial designs in 5 − 12nm technologies demonstrate that RL-CCD achieves up to 64% Total Negative Slack (TNS) reduction and 66.5% number of violating endpoints (NVE) improvement over the native implementation of a commercial tool. |
| Author | Guo, Deyuan Chan, Wei-Ting Kundu, Sudipto Khandelwal, Vishal Lim, Sung Kyu Lu, Yi-Chen |
| Author_xml | – sequence: 1 givenname: Yi-Chen surname: Lu fullname: Lu, Yi-Chen email: yclu@gatech.edu organization: Georgia Institute of Technology,School of ECE,Atlanta,GA – sequence: 2 givenname: Wei-Ting surname: Chan fullname: Chan, Wei-Ting email: wei-ting.chan@synopsys.com organization: Synopsys Inc.,Hillsboro,OR – sequence: 3 givenname: Deyuan surname: Guo fullname: Guo, Deyuan email: deyuan.guo@synopsys.com organization: Synopsys Inc.,Mountain View,CA – sequence: 4 givenname: Sudipto surname: Kundu fullname: Kundu, Sudipto email: sudipto.kundu@synopsys.com organization: Synopsys Inc.,Mountain View,CA – sequence: 5 givenname: Vishal surname: Khandelwal fullname: Khandelwal, Vishal email: vishal.khandelwal@synopsys.com organization: Synopsys Inc.,Hillsboro,OR – sequence: 6 givenname: Sung Kyu surname: Lim fullname: Lim, Sung Kyu email: limsk@gatech.edu organization: Georgia Institute of Technology,School of ECE,Atlanta,GA |
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| Snippet | Concurrent Clock and Data (CCD) optimization is a well-adopted approach in modern commercial tools that resolves timing violations using a mixture of clock... |
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| SubjectTerms | Charge coupled devices Delays Design automation Graph neural networks Optimization Reinforcement learning Transformers |
| Title | RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning |
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