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

由于片上功率密度的持续增加，当今的高性能处理器，甚至新兴的移动平台，比以往任何时候都受到更多的热限制。新兴的基于 Chiplet 的异构集成进一步加剧了热问题，因为堆叠集成的散热受到限制。温度升高会呈指数级降低半导体芯片的可靠性，因此是当今最令人担忧的问题之一。此外，长期可靠性对当前纳米集成电路（IC）的设计提出了重大挑战。为了应对这一趋势，大多数新一代处理器正在研究和实施运行时功耗、热量、资源和长期可靠性管理方案。然而，仍然有许多具有挑战性的问题需要解决，例如准确的全芯片运行时热量和功耗估计、依赖于工作负载的真正热点检测和预测、真正热点可靠性管理的运行时控制策略，以及热约束多核/众核和新兴小芯片设计中更智能的可靠性感知性能最大化等。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关注，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 芯片在晶体管尺寸不断缩小和功率密度不断增加的情况下变得更加强大和可靠。 该项目还将为基于新兴机器学习的方法的核心知识和技术做出重大贡献，用于多核/众核处理器的全芯片功耗、热建模和运行时控制和优化技术。该奖项将使研究人员能够接触更多女性和代表性不足的少数族裔学生，进一步为美国科技劳动力的多样性做出贡献。该项目通过利用商业多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以及智能运行时热/功率和可靠性管理技术。首先，该研究将为商用多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，例如循环神经网络 (RNN)、条件生成神经网络 (CGAN)、图神经网络 (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。其次，该项目还将基于所提议的基于 DNN 的热/功率/可靠性监视器并考虑实际控制方法，探索商用多核处理器和小芯片的基于学习的热/功率/可靠性管理。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

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.