SHF:Small: Data-Driven Thermal Monitoring and Run-Time Management for Manycore Processor and Chiplet Designs

当今的高性能处理器，甚至新兴的移动的平台，由于片上功率密度的持续增加，比以往任何时候都更加热约束。新兴的基于芯片的异构集成进一步加剧了热问题，因为堆叠集成导致散热受限。温度的增加指数地降低半导体芯片的可靠性，因此是当今的主要关注点之一。此外，长期可靠性代表了当前纳米集成电路（IC）设计的重大挑战。为了应对这一趋势，运行时电源，热，资源和长期可靠性管理计划正在研究和实施的大多数新一代的处理器。然而，仍有许多具有挑战性的问题需要解决，例如精确的全芯片运行时热和功率估计、依赖于工作负载的真实热点检测和预测、用于真实热点可靠性管理的运行时控制策略、以及热约束多/众核和新兴小芯片设计中的更智能的可靠性感知性能最大化，仅举几例。与此同时，基于深度神经网络（DNN）的深度学习正在获得巨大的吸引力，因为它们为许多具有挑战性和复杂的设计自动化问题提供了新的计算和优化范例。 该项目开发的新技术将使未来的VLSI芯片在持续的积极晶体管缩放和不断增加的功率密度中更加坚固和可靠。 该项目还将为新兴的基于机器学习的方法的核心知识和技术做出重大贡献，这些方法用于多/众核处理器的全芯片功率，热建模和运行时控制和优化技术。该项目将利用商用众核处理器的机器学习和数值方法的最新进展，探索新一代数据驱动的实时热监控和智能运行时热/功率和可靠性管理技术。首先，该研究将为商用众核处理器开发新的数据驱动的快速在线全芯片热和功率监控技术，以及考虑任意工作负载下实际散热器冷却条件的新兴小芯片设计。该项目将探索DNN网络的最新进展，如递归神经网络（RNN），条件生成神经网络（CGAN），图形神经网络（GNN）等，还将探索用于小芯片设计的可组合和可扩展的热建模。第二，该项目还将探索基于学习的热/功率/可靠性管理的商业众核处理器和chiplets基于拟议的DNN的热/功率/可靠性监视器考虑实用的控制方法。该奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

Today’s high-performance processors, and even emerging mobile platforms, are more thermally constrained than ever before due to continuing increase in on-chip power densities. Emerging Chiplet-based heterogeneous integration further exacerbates the thermal problems as heat dissipation is limited due to stacking integration. An increase in temperature exponentially degrades reliability of semiconductor chips and hence is one of the leading concerns today. Furthermore, long-term reliability represents a significant challenge for the design of current nanometer integrated circuits (ICs). To address this trend, runtime power, thermal, resource and long-term reliability management schemes are being studied and implemented in most new generations of processors. However, there are still many challenging problems to be solved such as accurate full-chip run-time thermal and power estimation, workload-dependent true hot-spot detection and prediction, run-time control policy for true hot-spot reliability management, and more intelligent reliability-aware performance maximization in a thermally-constrained multi/many-core and emerging chiplet designs, to name a few. At the same time, deep-learning-based on deep neural networks (DNN) are gaining significant traction, as they provide new computing and optimization paradigms for many of the challenging and complex design-automation problems. The new techniques developed in this project will make future VLSI chips more robust and reliable amid continued aggressive transistor scaling and increasing power density. This project will also contribute significantly to the core knowledge and technologies of emerging machine learning based approaches for full-chip power, thermal modeling and runtime control and optimization techniques for multi/many-core processors. This award will enable the investigator to engage with more female and underrepresented minority students to further contribute to the diversity in US science and technology workforce.This project explores a new generation of data-driven real-time thermal monitoring and smart run-time thermal/power and reliability management techniques by harnessing the latest advances in machine leaning and numerical methods for commercial many-core processors. First, the research will develop new data-driven fast online full-chip thermal- and power-monitoring techniques for commercial many-core processors, and emerging chiplet designs considering practical heat-sink cooling conditions under arbitrary workloads. The project will explore recent advances in DNN networks such as recurrent neural networks (RNN), conditional generative neural networks (CGAN), graph neural networks (GNN) etc. Composable and scalable thermal modeling will also be explored for chiplet design. Second, this project will also explore learning-based thermal/power/reliability management for commercial many-core processors and chiplets based on the proposed DNN-based thermal/power/ reliability monitors considering practical control approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.