LEVERAGING MACHINE LEARNING FOR FUNCTIONAL COVERAGE CLOSURE IN DESIGN VERIFICATION
Keywords:
Machine Learning Verification, Functional Coverage, Semiconductor Design, Protocol Validation, Coverage OptimizationAbstract
This comprehensive article explores the transformative role of machine learning in modern semiconductor design verification, focusing on functional coverage closure challenges and solutions. The article examines how ML technologies have revolutionized traditional verification methodologies, particularly in handling complex SoC designs and advanced protocols. The article encompasses multiple aspects, including predictive analytics for coverage optimization, reinforcement learning for test generation, and intelligent anomaly detection systems. Through detailed case studies of PCIe, CXL, and power management verification, the article demonstrates the significant improvements achieved through ML integration. The article also analyzes the benefits of ML-driven verification approaches while acknowledging the challenges in data management, model interpretability, and system maintenance.
References
Maryna Miroshnyk et al., "Overview of the modern SoC design technologies and open softprocessor architectures," 2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT), 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10416510
Wen Chen et al., "Challenges and Trends in Modern SoC Design Verification," ResearchGate, August 2017. [Online]. Available: https://www.researchgate.net/publication/318890041_Challenges_and_Trends_in_Modern_SoC_Design_Verification
Adam Quinn, "An Open-Source Framework for Rapid Validation of Scientific ASICs," arXiv:2406.15181 [physics.ins-det], 2024. [Online]. Available: https://arxiv.org/abs/2406.15181
Zixi Cui et al., "P4-Ace: Resource-Efficient Optimization and Verification for Programmable Switches," FMANO '24: Proceedings of the 2024 SIGCOMM Workshop on Formal Methods Aided Network Operation, Pages 8 - 1, 2024. [Online]. Available: https://dl.acm.org/doi/10.1145/3672199.3673884
Mahmoud Elbana, "Functional Verification using Machine Learning Techniques," ResearchGate, Feb. 2024. [Online]. Available: https://www.researchgate.net/publication/377919989_Functional_Verification_using_Machine_Learning_Techniques
Matthew Landers, Afsaneh Doryab, "Deep Reinforcement Learning Verification: A Survey," ACM Computing Surveys, Volume 55, Issue 14s, Article No.: 330, Pages 1 - 31, 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3596444
Vinay Kumar Pamula, Sai Raghavendra Mantripragada, "Implementation and verification of PCI express interface in a SoC," JStage, 2017. Available: https://www.jstage.jst.go.jp/article/comex/6/9/6_2017XBL0056/_article
Hiranmaye Chandu, "Advanced Methods for Verifying Memory Controllers in Modern Computing Systems," ResearchGate, 2024. [Online]. Available: https://www.researchgate.net/publication/384987499_Advanced_Methods_for_Verifying_Memory_Controllers_in_Modern_Computing_Systems
Nisha Bhadauriya Agarwal, Dr. Deepak Kumar Yadav, "A Comprehensive Analysis of Classical Machine Learning and Modern Deep Learning Methodologies," International Journal of Engineering Research & Technology, 2024. Available: https://www.ijert.org/a-comprehensive-analysis-of-classical-machine-learning-and-modern-deep-learning-methodologies
Khaled A. Ismail and Mohamed A. Abd El Ghany, "Survey on Machine Learning Algorithms Enhancing the Functional Verification Process," Electronics 2021, 10(21), 2688, 3 November 2021. Available: https://www.mdpi.com/2079-9292/10/21/2688
Monish Katari et al., "Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement," ResearchGate, 2024. [Online]. Available: https://www.researchgate.net/publication/382087944_Integration_of_AI_and_Machine_Learning_in_Semiconductor_Manufacturing_for_Defect_Detection_and_Yield_Improvement
Zijie J. Wang et al., "Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values," KDD '22: Proceedings of the 28th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining, Pages 4132 - 4142, 2022. Available: https://dl.acm.org/doi/10.1145/3534678.3539074
