Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, which is often a painstaking and intense process. In this work, we introduce LAUDE, a unified unit-test generation and debugging framework for hardware designs that cross-pollinates the semantic understanding of the design source code with the Chain-of-Thought (CoT) reasoning capabilities of foundational Large-Language Models (LLMs). LAUDE integrates prompt engineering and design execution information to enhance its unit test generation accuracy and code debuggability. We apply LAUDE with closed- and open-source LLMs to a large corpus of buggy hardware design codes derived from the VerilogEval dataset, where generated unit tests detected bugs in up to 100% and 93% of combinational and sequential designs and debugged up to 93% and 84% of combinational and sequential designs, respectively.
Complexity and Scale in AI-Assisted Workflow Management: A Federated Learning Case Study
RENCI, University of North Carolina at Chapel Hill, NC, USA（RENCI，北卡罗来纳大学教堂山分校）；Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA（信息科学研究所，南加州大学马里纳德尔雷耶斯分校）
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
Thrust of Data Science and Analytics, The Hong Kong University of Science and Technology (Guangzhou)（数据科学与分析 thrust，香港科学与技术大学（广州））；Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology（工业工程与决策分析系，香港科学与技术大学）；MOVENSYS Inc.（MOVENSYS公司）；University of Cologne（科隆大学）
Repository and Issue Characteristics of Early GenAI Adopters on GitHub
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Wakayama University（和歌山大学）；The University of Osaka（大阪大学）；University of British Columbia, Okanagan Campus（不列颠哥伦比亚大学奥肯那根校区）
SLEAN: Simple Lightweight Ensemble Analysis Network for Multi-Provider LLM Coordination: Design, Implementation, and Vibe Coding Bug Investigation Case Study
