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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(Public Law 117-2)。随着设计复杂性持续呈指数级增长，高效分析大型硬件设计时间的需求已成为设计关闭流程的主要瓶颈。为了减少较长的分析运行时间，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，但一个关键的根本挑战仍然没有解决：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，其可扩展性结果在8到16个CPU核心上基本停滞不前。下一代处理技术将以更复杂的场景为特色进行分析，从而导致计算复杂性大幅提高，远远超出现有的CPU并行STA解决方案的可扩展范围。因此，加速STA算法是电子设计自动化(EDA)工具的一个高度研究重点，以提高设计闭包流程的性能。这个职业项目创造了一个新的开源STA引擎，1)通过利用异构计算和机器学习的力量提供变革性的性能突破，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。拟议的研究和教育活动将促进技术转移，并使不同的产业界和学术界合作。这个职业项目研究新的STA算法，通过利用异质并行和机器学习的力量提供数量级的性能突破。它将从一个新的计算角度研究新的图形处理单元(GPU)内核算法和异构任务分解策略，以加速关键的STA问题，包括基于图的分析和基于路径的分析。此外，它还将建立一个基于学习的任务执行环境，以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。研究成果将实现对当前最先进技术的超快分析和优化算法，并显著改善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、机器学习和图形算法。该项目的结果将是开源的，以鼓励广泛的EDA研究人员和开发人员通过分享新的发现、想法和教育资源来为该项目做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。