CAREER: Robust Behavioral Fault Simulation Algorithms for Multilevel Simulation

本研究致力于通过使用并发仿真(CS)和多域并发仿真(MDCS)算法来改进仿真和测试方法。该项目开发了一个高效的行为故障仿真架构，该架构可以很好地集成结构模型和RTL模型。CS仅表示和存储来自无故障实验的独特行为的固有能力允许出色的内部观察和比较实验。此外，MDCS算法的使用为大型模拟提供了压缩技术，在支持创建多个实验场景的同时克服了内存瓶颈。这允许设计人员在任何模型中改变参数，并在单个模拟中对照其他模型配置对其进行模拟。根据这项拨款正在探索的活动包括：(A)开发使用CS和MDCS的行为故障模拟、可观察性和特征跟踪的算法；(B)通过增强的不稳定实验(无限循环)检测、使用MDCS算法的实验压缩和故障丢弃来减少内存需求。(C)评价能够无缝整合多层次模型的算法。

This research focuses on improving simulation and test methodologies through the use of Concurrent Simulation (CS) and Multiple Domain Concurrent Simulation (MDCS) algorithms. This project develops an efficient behavioral fault simulation architecture that integrates well with structural and RTL models. The inherent ability of CS to represent and store only unique behaviors from the fault free experiment allows for excellent internal observation and comparative experimentation. Furthermore, the use of MDCS algorithms provides compression techniques for large simulations, overcoming memory bottlenecks while supporting the creation of multiple experiment scenarios. This allows a designer to vary parameters within any model and simulate it against other model configurations in a single simulation. Activities being explored under this grant include: (a) Developing algorithms for behavioral fault simulation, observability and signature traces using CS and MDCS; (b) Reduction of memory requirements through enhanced erratic experiment (infinite loop) detection, experiment compression using MDCS algorithms and fault dropping. (c) Evaluation of algorithms that allow the seamless integration of multilevel models.