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

SHF:Small：适用于多核处理器和小芯片设计的数据驱动热监控和运行时管理

基本信息

批准号：
2113928
负责人：
Sheldon Tan
金额：
$ 50万
依托单位：
University of California-Riverside
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113928&HistoricalAwards=false
关键词：
SHF Small Data Driven Thermal

项目摘要

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.

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，都比以往任何时候都受到更多的热限制，因为新兴的基于 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。温度的升高会呈指数级降低半导体芯片的可靠性，因此是当今主要关注的问题之一。此外，长期可靠性对当前纳米集成电路（IC）的设计构成了重大挑战。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。但是，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/众核和新兴小芯片中更智能的可靠性感知性能最大化设计，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。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全芯片功率、热建模以及运行时控制和优化技术的方法的核心知识和技术做出重大贡献。适用于多核/众核处理器。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。该项目探索了新一代数据驱动的实时热监测和智能运行时热/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量和功率监控技术，通过利用商用多核处理器的机器学习和数值方法的最新进展来开发电源和可靠性管理技术。处理器和新兴小芯片设计考虑到该项目将探索 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。可组合和可扩展的热建模将其次，该项目还将探索基于学习的商用多核处理器和小芯片的热/功率/可靠性管理，基于所提出的基于 DNN 的热/功率/可靠性监视器，考虑到实际控制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Electrothermal Simulation and Optimal Design of Thermoelectric Cooler Using Analytical Approach

采用解析方法的热电冷却器的电热仿真和优化设计

DOI：
10.1109/tcad.2021.3120533
发表时间：
2022-09
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Chen, Liang;Sadiqbatcha, Sheriff;Amrouch, Hussam;Tan, Sheldon X.
通讯作者：
Tan, Sheldon X.

Scaled-CBSC: Scaled counting-based stochastic computing multiplication for improved accuracy

Scaled-CBSC：基于缩放计数的随机计算乘法以提高准确性

DOI：
发表时间：
2022-07
期刊：
Proc. IEEE/ACM Design Automation Conference (DAC’22
影响因子：
0
作者：
Yu, S.;Tan, S.
通讯作者：
Tan, S.

Hot-Trim: Thermal and Reliability Management for Commercial Multi-core Processors Considering Workload Dependent Hot Spots

Hot-Trim：考虑工作负载相关热点的商用多核处理器的热和可靠性管理

DOI：
10.1109/tcad.2022.3216552
发表时间：
2023-01
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Zhang, Jinwei;Sadiqbatcha, Sheriff;Tan, Sheldon X.
通讯作者：
Tan, Sheldon X.

Fast thermal analysis for chiplet design based on graph convolution networks

基于图卷积网络的小芯片设计快速热分析