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: 2144523
• 负责人: Tsung-Wei Huang
• 金额: $ 50.04万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144523&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（Public Law 117-2）资助。随着设计复杂性继续呈指数级增长，高效分析大型硬件设计时序的需求已成为设计关闭流程的主要瓶颈。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未解决：几乎所有现有的并行STA解决方案在架构上都受到中央处理器（CPU）并行性的限制，其可扩展性在8到16个CPU内核时基本稳定。下一代工艺技术将以更复杂的场景为特征进行分析，从而导致计算复杂性的数量级更高，远远超过现有CPU并行STA解决方案的规模。因此，加速STA算法是电子设计自动化（EDA）工具的一个高优先级的研究，以提高设计闭合流的性能。这个CAREER项目创建了一个新的开源STA引擎，1）通过利用异构计算和机器学习的力量实现转型性能突破，2）为研究人员建立一个开放平台，为设计自动化研究和教育做出贡献。建议的研究和教育活动将促进技术转让，并实现多样化的产学合作。该CAREER项目研究新型STA算法，通过利用异构并行和机器学习的力量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，以加速关键STA问题，包括基于图的分析和基于路径的分析，从一个新的计算角度。此外，它将建立一个基于学习的任务执行环境，以实现自适应性能优化，以不同的STA工作负载下的真实的操作条件。研究成果将使超快速的分析和优化算法超过目前的最先进的，并大大提高周转时间和质量的结果（QoR）的设计封闭流。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，机器学习和图形算法。该项目的成果将开放源代码，以鼓励广泛的EDA研究人员和开发人员通过分享新的发现，想法和教育资源为项目做出贡献。该奖项反映了NSF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。