Constraint-Based Verification covers an emerging field in functional verification of electronic designs, referred to as the "constraint-based verification." The topics are developed in the context of a wide range of dynamic and static verification approaches including simulation, emulation, and formal methods. The goal is to show how constraints, or assertions, can be used towards automating the generation of testbenches, resulting in a seamless verification flow. Topics such as verification coverage, and connection with assertion based verification, are also covered.
The book targets verification engineers as well as researchers. It covers both methodological and technical issues. Particular stress is given to the latest advances in functional verification.
The research community has witnessed recent growth of interests in constraint-based functional verification. Various techniques have been developed. They are relatively new, but have reached a level of maturity so that they are appearing in commercial tools such as Vera and System Verilog.
Edition
Softcover reprint of hardcover 1st ed. 2006
Language
Place of publication
Target group
Professional and scholarly
Research
Illustrations
72 s/w Abbildungen
XII, 254 p. 72 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
ISBN-13
978-1-4419-3852-7 (9781441938527)
DOI
Schweitzer Classification
Shixia Liu is a professor at Tsinghua University. Her research interests include explainable machine learning, visual text analytics, and text mining. Shixia was elevated to an IEEE Fellow in 2021 and inducted into IEEE Visualization Academy in 2020. She is an associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics and is an associate editor of Artificial Intelligence, IEEE Transactions on Big Data, and ACM Transactions on Intelligent Systems and Technology. She was one of the Papers Co-Chairs of IEEE VIS (VAST) 2016 and 2017 and is in the steering committee of IEEE VIS (2020-2023).
Weikai Yang is an Assistant Professor at the Data Science and Analytics Trust, holding a joint appointment at the Computational Media and Arts Thrust (CMA) in the Information Hub, at The Hong Kong University of Science and Technology (Guangzhou). He received his Ph.D. in Software Engineering under the supervision of professor Shixia Liu and his B.S. degrees from Tsinghua University. His research primarily focuses on the intersections between visual analysis and machine learning, with the goal of helping general users to understand large-scale data and utilize machine learning models more effectively and efficiently by incorporating their knowledge and feedback.
Junpeng Wang is a Research Scientist at Visa Research. He received his B.Eng. degree in software engineering from Nankai University in 2011, his M.S. degree in computer science from Virginia Tech in 2015, and his Ph.D. degree in computer science from the Ohio State University in 2019. Junpeng's research interests lie broadly in explainable artificial intelligence, visual analytics, and deep learning. He is the recipient of the 2021 IEEE TVCG Best Reviewer Award and multiple best paper awards, including the Best Paper Award at IEEE PacificVis 2018, the Best Paper Honorable Mention Award at IEEE VIS (VAST) 2018, and the Best Paper Award at IEEE VIS (SciVis) 2019.
Jun Yuan is a Researcher at Tencent. His research interests lie in explainable artificial intelligence. He received his Ph.D. in Software Engineering under the supervision of Professor Shixia Liu and his B.S. degrees from Tsinghua University.
Constrained Random Simulation.- High Level Verification Languages.- Assertion Languages and Constraints.- Preliminaries.- Constrained Vector Generation.- Constraint Simplification.- More Optimizations.- Constraint Synthesis.- Constraint Diagnosis.- Word-Level Constraint Solving.
