REVOLUTIONIZING STATIC TIMING ANALYSIS THROUGH MACHINE LEARNING INTEGRATION
Keywords:
Machine Learning, Static Timing Analysis, Electronic Design Automation, Semiconductor Design, Predictive ModelingAbstract
The rapid evolution of Machine Learning (ML) in Static Timing Analysis (STA) represents a transformative approach to electronic design automation. This comprehensive article explores the integration of advanced ML techniques into semiconductor design workflows, demonstrating significant improvements in timing prediction, computational efficiency, and design verification processes. By leveraging sophisticated neural network architectures and deep learning methodologies, researchers have developed innovative approaches that fundamentally reshape traditional timing analysis methodologies, offering unprecedented insights into circuit performance and optimization strategies.
References
Team Kissflow, "Workflow Optimization Tips To Sharpen Up Your Business Processes," Kissflow Digital Workplace, 2024. [Online]. Available: https://kissflow.com/workflow/workflow-optimization-tips-to-sharpen-business-processes/
Zhenyu Zhao, et al., "Machine-Learning-Based Multi-Corner Timing Prediction for Faster Timing Closure," Electronics, 2022. [Online]. Available: https://www.mdpi.com/2079-9292/11/10/1571
Lihua Dai, et al., "The application of deep learning technology in integrated circuit design," Energy Informatics, 2024. [Online]. Available: https://energyinformatics.springeropen.com/articles/10.1186/s42162-024-00380-w
Vidya A. Chhabria, "A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route," ACM Transactions on Design Automation of Electronic Systems, 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3626959
Marco Mandolini, et al., "A cost modelling methodology based on machine learning for engineered-to-order products" Engineering Applications of Artificial Intelligence, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197624011151
Winner Olabiyi, "APPLYING MACHINE LEARNING FOR TIMING ANALYSIS IN DIGITAL CIRCUITS USING PYRTL," ResearchGate Technical Report, 2024. [Online]. Available: https://www.researchgate.net/publication/385130798_APPLYING_MACHINE_LEARNING_FOR_TIMING_ANALYSIS_IN_DIGITAL_CIRCUITS_USING_PYRTL
Marco Mandolini, et al., “A cost modelling methodology based on machine learning for engineered-to-order products," Engineering Applications of Artificial Intelligence, 2024. Available: https://www.sciencedirect.com/science/article/pii/S0952197624011151
Nico Klingler, “Typical Workflow for Building a Machine Learning Model” Viso AI, 2024. Available: https://viso.ai/computer-vision/typical-workflow-for-building-a-machine-learning-model/
Xuanyi Tan, et al., "ML-TIME: ML-driven Timing Analysis of Integrated Circuits in the Presence of Process Variations and Aging Effects," Proceedings of the ACM Conference on Design Automation, 2024. [Online]. Available: https://dl.acm.org/doi/10.1145/3670474.3685968
Tingyuan Nie, et al., "Machine Learning Method for Predicting Circuit Timing Using Complex Network," Social Science Research Network (SSRN), 2024. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744638
Duan-Yang Liu, et al., "Machine learning for semiconductors," Chip, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2709472322000314
Guyue Huang, et al., "Machine Learning for Electronic Design Automation: A Survey," ResearchGate, 2021. [Online]. Available: https://www.researchgate.net/publication/349106567_Machine_Learning_for_Electronic_Design_Automation_A_Survey
