CAREER: HeteroTime: Accelerating Static Timing Analysis with Intelligent Heterogeneous Parallelism

职业：HeteroTime：利用智能异构并行加速静态时序分析

• 批准号: 2349582
• 负责人: Tsung-Wei Huang
• 金额: $ 50.04万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349582&HistoricalAwards=false
• 关键词: CAREER; HeteroTime; Accelerating; Static; Timing

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).As design complexity continues to grow exponentially, the need to efficiently analyze the timing of large hardware designs has become the major bottleneck to the design closure flow. To reduce long analysis runtimes, recent years have seen many parallel static timing analysis (STA) solutions. Despite improved performance, a key fundamental challenge remains unsolved: Almost all existing parallel STA solutions are architecturally constrained by central processing unit (CPU) parallelism, and their scalability results largely plateaued at 8 to 16 CPU cores. Next-generation process technologies will feature more complex scenarios to analyze, resulting in order-of-magnitude higher computational complexity that far exceeds what existing CPU-parallel STA solutions can scale to. Speeding up STA algorithms is thus a high research priority for electronic design automation (EDA) tools to boost the performance of design closure flows. This CAREER project creates a novel open-source STA engine that 1) delivers transformational performance breakthroughs by harnessing the power of heterogeneous computing and machine learning and 2) establishes an open platform for researchers to contribute to design automation research and education. The proposed research and education activities will facilitate technology transfers and enable diverse industry-academia collaborations.This CAREER project researches novel STA algorithms that deliver order-of-magnitude performance breakthroughs by harnessing the power of heterogeneous parallelism and machine learning. It will research novel graphics processing unit (GPU) kernel algorithms and heterogeneous task decomposition strategies to accelerate critical STA problems, including graph-based analysis and path-based analysis, from a novel computing perspective. Furthermore, it will establish a learning-based task execution environment to achieve adaptive performance optimization to different STA workloads under real operating conditions. The research outcomes will enable ultra-fast analysis and optimization algorithms over the current state-of-the-art and substantially improve both turnaround time and quality of results (QoR) for design closure flows. Technical contributions of this project will span a multidisciplinary research community, including EDA, parallel computing, machine learning, and graph algorithms. Results of the project will be made open-source to encourage a wide range of EDA researchers and developers to contribute to the project by sharing new findings, ideas, and educational resources.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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。随着设计复杂度呈指数级增长，高效分析大型硬件设计的时序需求已成为设计闭合流程的主要瓶颈。为了减少长时间的分析运行时间，近年来出现了许多并行静态时序分析（STA）解决方案。尽管性能有所提高，但一个关键的基本挑战仍然没有得到解决：几乎所有现有的并行STA解决方案在体系结构上都受到中央处理单元（CPU）并行性的限制，它们的可伸缩性结果在8到16个CPU内核时基本停滞不前。下一代工艺技术将以更复杂的场景为特征进行分析，其计算复杂性将远远超过现有的cpu并行STA解决方案所能扩展的数量级。因此，加速STA算法是电子设计自动化（EDA）工具提高设计闭合流性能的一个重要研究重点。这个CAREER项目创建了一个新颖的开源STA引擎，1)通过利用异构计算和机器学习的力量提供变革性的性能突破，2)为研究人员建立一个开放的平台，为设计自动化研究和教育做出贡献。拟议的研究和教育活动将促进技术转让，并使不同的产学研合作成为可能。这个CAREER项目研究新的STA算法，通过利用异构并行和机器学习的力量来实现数量级的性能突破。它将研究新的图形处理单元（GPU）内核算法和异构任务分解策略，以从新的计算角度加速关键STA问题，包括基于图的分析和基于路径的分析。建立基于学习的任务执行环境，实现对真实运行条件下不同STA工作负载的自适应性能优化。该研究成果将实现超快速分析和优化算法，大大提高设计关闭流程的周转时间和结果质量（QoR）。该项目的技术贡献将跨越多学科研究社区，包括EDA，并行计算，机器学习和图算法。这个项目的成果将是开源的，以鼓励更多的EDA研究人员和开发人员通过分享新的发现、想法和教育资源来为这个项目做出贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。