International Journal of Advanced Artificial Intelligence Research

  1. Home
  2. Archives
  3. Vol. 2 No. 11 (2025): Volume 02 Issue 11
  4. Articles
International Journal of Advanced Artificial Intelligence Research

Article Details Page

The Convergence of AI And UVM: Advanced Methodologies for the Verification of Complex Low-Power Semiconductor Architectures

Authors

  • Angelo soriano Electrical and Electronics Engineering Institute, University of the Philippines Diliman, Quezon City, Philippines
  • Sheila Ann Mercado Electrical and Electronics Engineering Institute, University of the Philippines Diliman, Quezon City, Philippines

DOI:

https://doi.org/10.55640/

Keywords:

Low-Power Design, Semiconductor Verification, Universal Verification Methodology (UVM), Artificial Intelligence (AI), System-on-Chip (SoC), Dynamic Voltage and Frequency Scaling (DVFS)

Abstract

Purpose: The exponential growth of complex semiconductor architectures, particularly for IoT, AI, and mobile computing, has made power consumption the primary design constraint. Low-power design techniques (LPDTs) like Dynamic Voltage and Frequency Scaling (DVFS), power gating, and clock gating, introduce significant verification challenges that traditional methodologies cannot adequately address. This article analyzes the existing "verification gap" and proposes an integrated methodological framework.

Methodology: This work conducts a comprehensive methodological review of current and emerging verification strategies. It analyzes the limitations of the standard Universal Verification Methodology (UVM) and conventional Design for Test (DFT) in low-power contexts. We then synthesize a novel framework integrating advanced UVM strategies (UVM-LP) with Artificial Intelligence (AI) and Machine Learning (ML) driven analytics.

Findings: The analysis indicates that standard UVM struggles with the state-space explosion of power domains and transitions. AI-driven approaches, including predictive analytics for test generation and active learning for power state analysis, show significant potential to optimize verification efforts, enhance coverage of critical corner cases, and reduce time-to-market. The synergy between UVM's structured environment and AI's intelligent optimization provides a robust solution.

Originality/Value: This article presents a holistic, integrated framework for low-power verification. It bridges the gap between structured verification (UVM) and intelligent automation (AI), offering a forward-looking perspective on managing the immense complexity of modern System-on-Chip (SoC) low-power design verification.

 

References

Amelia, O. (2024). AI-Driven Testing and Validation Techniques for Low-Power Semiconductor Design Verification Using UVM.

Antonio, R. A., de la Costa, R. M., Ison, A. R., Lim, W. K., Pajado, R. A., Roque, D. B., ... & Alarcon, L. (2017, November). Implementation of dynamic voltage frequency scaling on a processor for wireless sensing applications. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 2955-2960). IEEE.

Bambagini, M., Marinoni, M., Aydin, H., & Buttazzo, G. (2016). Energy-aware scheduling for real-time systems: A survey. ACM Transactions on Embedded Computing Systems (TECS), 15(1), 1-34.

Bindra, A., & Mantooth, A. (2019). Modern tool limitations in design automation: Advancing automation in design tools is gathering momentum. IEEE Power Electronics Magazine, 6(1), 28-33.

Byna, S., Idreos, S., Jones, T., Mohror, K., Ross, R., & Rusu, F. (2022). Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data. US Department of Energy (USDOE), Washington DC (United States). Office of Science.

Çakmak, R. (2024). Design and implementation of a low-cost power logger device for specific demand profile analysis in demand-side management studies for smart grids. Expert Systems with Applications, 238, 121888.

Building Compliance-Driven AI Systems: Navigating IEC 62304 and PCI-DSS Constraints. (2025). International Journal of Networks and Security, 5(01), 62-90. https://doi.org/10.55640/ijns-05-01-06

Casolino, G. M., Russo, M., Varilone, P., & Pescosolido, D. (2018). Hardware-in-the-loop validation of energy management systems for microgrids: A short overview and a case study. Energies, 11(11), 2978.

Chakravarthi, V. S. (2020). A practical approach to VLSI system on chip (SoC) design. Springer International Publishing.

Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices

Chavan, A., & Romanov, Y. (2023). Managing scalability and cost in microservices architecture: Balancing infinite scalability with financial constraints. Journal of Artificial Intelligence & Cloud Computing, 5, E102. https://doi.org/10.47363/JMHC/2023(5)E102

Chen, Y. T., Cong, J., Fang, Z., Lei, J., & Wei, P. (2016). When Spark Meets {FPGAs}: A Case Study for {Next-Generation}{DNA} Sequencing Acceleration. In 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16).

Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time. Journal of Computer Science and Technology Studies, 6(5), 246-264. https://doi.org/10.32996/jcsts.2024.6.5.20

Dias, P. R., Schmidt, L., Chang, N. L., Lunardi, M. M., Deng, R., Trigger, B., ... & Veit, H. (2022). High yield, low cost, environmentally friendly process to recycle silicon solar panels: Technical, economic and environmental feasibility assessment. Renewable and Sustainable Energy Reviews, 169, 112900.

Gill, P. (2016). Electrical power equipment maintenance and testing. CRC press.

Gill, S. S., Kumar, A., Singh, H., Singh, M., Kaur, K., Usman, M., & Buyya, R. (2022). Quantum computing: A taxonomy, systematic review and future directions. Software: Practice and Experience, 52(1), 66-114.

Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies. International Journal of Science and Research Archive, 13(2), 2155. https://doi.org/10.30574/ijsra.2024.13.2.2155

Jain, A., Shin, Y., & Persson, K. A. (2016). Computational predictions of energy materials using density functional theory. Nature Reviews Materials, 1(1), 1-13.

Karwa, K. (2024). The role of AI in enhancing career advising and professional development in design education: Exploring AI-driven tools and platforms that personalize career advice for students in industrial and product design. International Journal of Advanced Research in Engineering, Science, and Management. https://www.ijaresm.com/uploaded_files/document_file/Kushal_KarwadmKk.pdf

Koenemann, B. (2018). Design-for-test. In EDA for IC System Design, Verification, and Testing (pp. 21-1). CRC Press.

Sai Nikhil Donthi. (2025). A Scrumban Integrated Approach to Improve Software Development Process and Product Delivery. The American Journal of Interdisciplinary Innovations and Research, 7(09), 70–82. https://doi.org/10.37547/tajiir/Volume07Issue09-07

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Li, D., Chen, X., Becchi, M., & Zong, Z. (2016, October). Evaluating the energy efficiency of deep convolutional neural networks on CPUs and GPUs. In 2016 IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom)(BDCloud-SocialCom-SustainCom) (pp. 477-484). IEEE.

Liu, S., & Karanth, A. (2021, December). Dynamic voltage and frequency scaling to improve energy-efficiency of hardware accelerators. In 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) (pp. 232-241). IEEE.

Low-Power Semiconductor Design: AI and UVM-Based Verification Strategies.

Mahmood, S., Sun, H., Ali Alhussan, A., Iqbal, A., & El-Kenawy, E. S. M. (2024). Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption. Scientific Reports, 14(1), 19894.

Nagaraj, V. (2025). Ensuring low-power design verification in semiconductor architectures. Journal of Information Systems Engineering and Management, 10(45s), 703–722. https://doi.org/10.52783/jisem.v10i45s.8903

Meixner, A., & Gullo, L. J. (2021). Design for Test and Testability. Design for Maintainability, 245-264.

Mishra, A., & Khare, N. (2015). Analysis of DVFS techniques for improving the GPU energy efficiency. Open Journal of Energy Efficiency, 4(4), 77-86.

Naveen Balaji, G., & Chenthur Pandian, S. (2019). Design of test pattern generator (TPG) by an optimized low power design for testability (DFT) for scan BIST circuits using transmission gates. Cluster Computing, 22, 15231-15244.

Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230

Ouni, B., Mhedbi, I., Trabelsi, C., Atitallah, R. B., & Belleudy, C. (2017). Multi-level energy/power-aware design methodology for MPSoC. Journal of Parallel and Distributed Computing, 100, 203-215.

Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf

Rodrigues, A. O. (2021). A Framework for Mobile Augmented Reality in Urban Maintenance (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

Rong, H., Zhang, H., Xiao, S., Li, C., & Hu, C. (2016). Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58, 674-691.

Saad, S., Haris, M., Ammad, S., & Rasheed, K. (2024). AI-assisted building design. In AI in material science (pp. 143-168). CRC Press.

Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253

Singh, V. (2022). EDGE AI: Deploying deep learning models on microcontrollers for biomedical applications: Implementing efficient AI models on devices like Arduino for real-time health monitoring. International Journal of Computer Engineering & Management. https://ijcem.in/wp-content/uploads/EDGE-AI-DEPLOYING-DEEP-LEARNING-MODELS-ON-MICROCONTROLLERS-FOR-BIOMEDICAL-APPLICATIONS-IMPLEMENTING-EFFICIENT-AI-MODELS-ON-DEVICES-LIKE-ARDUINO-FOR-REAL-TIME-HEALTH.pdf

Singh, V. (2023). Federated learning for privacy-preserving medical data analysis: Applying federated learning to analyze sensitive health data without compromising patient privacy. International Journal of Advanced Engineering and Technology, 5(S4). https://romanpub.com/resources/Vol%205%20%2C%20No%20S4%20-%2026.pdf

Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2023). Comparative study of FPGA and GPU for high-performance computing and AI. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1), 37-46.

Wang, X., Zhang, D., He, M., Su, D., & Tehranipoor,

M. (2017). Secure scan and test using obfuscation throughout supply chain. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(9), 1867-1880.

Zeltmann, S. E., Gupta, N., Tsoutsos, N. G., Maniatakos, M., Rajendran, J., & Karri, R. (2016, November). Manufacturing and security challenges in 3D integrated circuits. In 2016 IEEE-CS Annual Symposium on VLSI (ISVLSI) (pp. 417-422). IEEE.

Vikram Singh, 2025, Policy Optimization for Anti- Money Laundering (AML) Compliance using AI Techniques: A Machine Learning Approach to Enhance Banking Regulatory Compliance, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY

(IJERT) Volume 14, Issue 04 (April 2025)

Downloads

Published

2025-11-02

How to Cite

The Convergence of AI And UVM: Advanced Methodologies for the Verification of Complex Low-Power Semiconductor Architectures. (2025). International Journal of Advanced Artificial Intelligence Research, 2(11), 12-22. https://doi.org/10.55640/

How to Cite

The Convergence of AI And UVM: Advanced Methodologies for the Verification of Complex Low-Power Semiconductor Architectures. (2025). International Journal of Advanced Artificial Intelligence Research, 2(11), 12-22. https://doi.org/10.55640/

Similar Articles

21-30 of 30

You may also start an advanced similarity search for this article.