Hardware Verification
Advancing methodologies for verifying digital circuits, including formal approaches for automated testbench component generation and simulation-based techniques for complex hardware systems.
Texas A&M University
Veritas Lab is a state-of-the-art research facility advancing computer science and engineering through pioneering work in semiconductor chip testing, verification, and validation. We bring together world-class faculty and talented students to address critical challenges in the field, with a strong emphasis on applying artificial intelligence to achieve transformative gains in design quality and operational efficiency. Veritas partners with leading chip design companies as well as federal and state agencies, fostering applied research that bridges theory and practice.
Advancing methodologies for verifying digital circuits, including formal approaches for automated testbench component generation and simulation-based techniques for complex hardware systems.
Investigating the use of artificial intelligence to enhance the quality and efficiency of physical design and electronic design automation (EDA) methodologies.
Exploring the intersection of AI and hardware verification by developing machine learning–based techniques to strengthen both pre-silicon and post-silicon verification practices.
Conducting research in automated test generation, mutation testing, and program analysis to improve software quality and reliability across diverse application domains.
Veritas signed a 1.28M USD contract with Natcast under the auspices of the US Department of Commerce. The funding will support Workforce Advancement in Verification and Evaluation of Chips (WAVE-CHIP), a program designed to…
Veritas organized a train-the-trainer workshop during August 11-15, 2025 at the Texas A&M University Campus to facilitate instructor training in hardware verification. The workshop was attended by 15 instructors from neighboring R1/R2 institutions.
Congratulations to recent graduates Minh Luu and Surya Jasper, and continuing PhD student Chengjia Liu for publishing their research at the 7th ACM/IEEE International Symposium on Machine Learning for CAD.