Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 28, 26 December 2023
Open
Access | Article
Machine learning in physical design
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Abstract
Machine learning is a highly effective instrument in constructing models that can expeditiously produce accurate prognostications. As the complexity of integrated circuit design continues to increase and process nodes continue to evolve, and physical design faces more challenges from modeling and optimization. To address these challenges, machine learning has been introduced into physical design. Thus, in this paper, we discuss the application of machine learning in physical design, covering topics such as Clock Tree Synthesis (CTS), Placement and Routing, IR-Drop and Static Timing Analysis (STA). The essay explores how machine learning can be used to overcome challenges in these areas, such as reducing peak current and clock skew in CTS, optimizing placement parameters and decision-making, predicting routability and reducing IR-drop effects. This paper also discusses various machine learning techniques (ML), such as reinforcement learning, convolutional neural networks and transfer learning. To conclude, we provide insights into how machine learning can be applied to improve various aspects of physical design.
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-261-9
- ISBN (Online)
- 978-1-83558-262-6
- Published Date
- 26 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/28/20230384
- Copyright
- 26 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited