报告题目：Why, Can, and How Research?– personal experience sharing with beginners报告时间：3月24日 10:00线下报告地点：数学馆201主持人：王廷报告摘要：In this information age, IT industry associates, e.g., senior undergraduates, masters, and junior PhDs, may have questions like why do research, can I do excellent research, and how to do high quality research. We would like to share personal understanding on these questions with young and energetic researchers. Firstly, research will enhance our capability, which is totally differ

报告题目：芯片功能性验证的半形式化方法报告时间：3月23日 10:00线下报告地点：数学馆211主持人：陈铭松报告摘要：芯片功能的验证复杂性随着功能越来越丰富呈指数型增长。在芯片模型阶段及早发现和处理芯片设计中存在的问题可以有效避免流片后修正带来的巨大的经济和时间损失。尽管EDA中的验证工具不断完善，大量无法覆盖的边界状态和分支仍然存在，这些往往是芯片中缺陷和安全隐患的主要来源。传统的形式化方法虽能定向生成覆盖目标的测试向量，但是随着模型的复杂度增加会有状态爆炸的问题。半形式化方法结合了仿真的可扩展性与形式化方法的定向覆盖的双重优势，利用路径搜索的办法生成可以覆盖边界情况的测试。本报告简要地回顾芯片功能验证中的挑战，比较几种常见的验证方法，并介绍本团队研究的半形式化方法。报告人简介：吕杨迪，香港科技大学（广州）功能枢纽微电子学域助理教授。2020年获得美国佛罗里达大学计算机科学博士学位。毕业后曾在美国硅谷谷歌公司担任软件工程师。2021年8月加入香港科技大学（广州）。主要研究领域是计算机系统安全验证方法，包含了芯片自动化设计工具、自动化测试生产方法及硬件安全与信任。已在国际知名会议及杂

Title: 第20届CCF全国嵌入式系统大会(CCF ESTC 2022)活动流程<br> Time： 08:30-19:00, October9 Sunday,2022<br> Location: <br> Lecturer：魏同权、邹桉、张凯龙、邓庆绪、董云卫、陈仪香、周俊龙

报告题目：Temporal Satisfiability Modulo Theories报告时间：11月2日 xx:00-xx：xx线下报告地点：理科大楼B1002腾讯会议号：主持人：李建文报告摘要：Checking temporal satisfiability (or validity) is a fundamental problem to make formal verification scale up with compositional reasoning. In this talk, we focus on LTL satisfiability modulo theories, which generalizes the satisfiability problem for LTL to the first-order case with respect to some background theories. Compositional reasoning takes into account both synchronous and asynchronous int

报告题目：Data Science for Deep Learning报告时间：10月20日 11:00-12：30线下报告地点：理科大楼B1002腾讯会议号：980181758主持人：程鹏报告摘要：In recent years, deep learning (DL) has significantly penetrated and has been widely adopted in various fields of application, including facial recognition, strategy games (AlphaGo and Texas holdem) and question answering. However, the effectiveness of the models and efficiency of the training process strongly depend on how well the associated data is managed. It is very challenging to train an ef

报告题目：Reachability Analysis of Learning-Enabled Cyber-Physical Systems报告时间：12月29日 09:00线下报告地点：理科大楼B002腾讯会议号：763-614-103主持人：张民教授报告摘要：Neural networks have been increasingly used as the central decision makers in a variety of tasks. However, the use of neural-network controllers also gives rise to new challenges on verifying the correctness of the resulting closed-loop control systems especially in safety-critical settings. This talk focuses on computing the reachable sets of Cyber-Physical Systems