Jun Yuan - Böcker

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.

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.

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering;

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. It shows how visualization can bridge the gap between complex models or algorithms and human understanding while also facilitating data curation and model refinement. Therefore, visualization for artificial intelligence (VIS4AI) has become an emerging area that combines interactive visualization with machine learning techniques to maximize their values. VIS4AI techniques focus on every phase of the machine learning life cycle, from data preprocessing to model development and deployment. These techniques are closely aligned with the well-established data and model pipelines in machine learning. In the data pipeline, they contribute to improving data quality and feature quality, including training data cleaning and feature engineering. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering; and (2) model deployment by enabling decision explanation, model performance monitoring, and model maintenance. This book provides a framework of VIS4AI and introduces the associated techniques in the two pipelines. It emphasizes the importance of interactive visualization in AI and presents various visualization techniques for different purposes. It also discusses the challenges and opportunities of VIS4AI and proposes several promising research topics for future work, such as improving training data using complementary modalities, online training diagnosis, fitting the dynamic nature of AI systems, and interactively pre-training and adapting foundation models. Overall, this book aims to serve as a resource for researchers and practitioners interested in both visualization and artificial intelligence.