Thermal-Sensitive System-Level Reliability Analysis and Management for Multi-Core and 3D Microprocessors

可靠性已成为当前多核和新兴3D微处理器设计的一个重大挑战。许多长期失效机制，如电迁移、应力迁移和热循环对温度或温度变化非常敏感。因此，在微建筑设计阶段，迫切需要对可靠性进行认识和优化。由于温度对许多故障问题具有指数级影响，因此对可靠性分析甚至架构和封装级别的优化进行准确快速的热估计至关重要。在这个项目中，主要研究者（PI）首先计划开发架构级全芯片可靠性建模和分析技术，考虑集成技术的新结构和主要的硬失效机制。然后，PI将为多核和3D堆叠微处理器开发可靠性感知动态热管理技术。最后，PI将开发全芯片热估计和预测技术，考虑到现实条件，如有限的物理热传感器和热和功率模型中存在的错误，用于运行时系统级可靠性分析和优化。这项研究的结果将显著增加系统级可靠性优化技术的核心知识，这将使多核和3D微处理器的热优化和设计更加有效。该项目的跨学科性质将使本科生和研究生获得对他们未来在STEM领域的努力有价值的独特技能。这笔拨款将使PI能够雇用更多的女性和代表性不足的少数民族学生，进一步促进科学和技术劳动力的多样性。

Reliability has become a significant challenge for the current multi-core and emerging 3D microprocessor design. Many long-term failure mechanisms such as electromigration, stress migration, and thermal-cycling are very sensitive to temperature or to temperature changes. As a result, there is an urgent need for reliability awareness and optimization at the micro-architectural design stage. Since temperature has exponential impacts on many failure issues, it is crucial to have accurate and fast thermal estimation for reliability analysis and even optimization at the architecture and package levels. In this project, the principle investigator (PI) first plans to develop architecture-level full-chip reliability modeling and analysis techniques considering new structures of integration techniques and dominant hard failure mechanisms. Then the PI will develop reliability-aware dynamic thermal management techniques for multi-core and 3D stacking microprocessors. Finally, the PI will develop full-chip thermal estimation and prediction techniques considering realistic conditions such as limited physical thermal sensors and presence of errors in thermal and power models, for run time system-level reliability analysis and optimization. The outcome of this research will add significantly to the core knowledge of system-level reliability optimization techniques, which will enable more efficient thermal optimization and design of multi-core and 3D microprocessors. The interdisciplinary nature of the project will enable undergraduate and graduate students to acquire unique skills valuable to their future endeavors in STEM fields. This grant will enable the PI to hire more female and underrepresented minority students to further contribute to diversity in the science and technology workforce.