Collaborative Research: SHF: Small: Sub-millisecond Topological Feature Extractor for High-Rate Machine Learning

合作研究：SHF：小型：用于高速机器学习的亚毫秒拓扑特征提取器

• 批准号: 2234921
• 负责人: Austin Downey
• 金额: $ 25万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234921&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Sub

High-rate systems are defined as dynamic systems experiencing high-rate and high-amplitude events. Examples include hypersonic vehicles and active impact mitigation strategies. The advanced operation of these mechanisms can only be achieved through control and feedback systems capable of operating in the sub-millisecond range, thus necessitating tight performance constraints. Additionally, high-rate systems are highly nonlinear and nonstationary, for which traditional real-time inference methods are incapable of providing credible predictions. Topological data analysis is gaining popularity for classifying complex time series. Its integration with architected machine learning algorithms shows promise in advancing the predictive capabilities for high-rate systems. However, topological data analysis is computationally expensive and cannot be applied in the sub-millisecond range. This project will investigate real-time topological data analysis capabilities by developing and integrating advances in mathematical, software, and hardware foundations. Successful completion of this project will yield theoretical foundations enabling the integration of topological data analysis with machine learning for modeling and forecasting time series, constituting a major leap from the pure algebraic topological approach. It is envisioned that the developed foundations, along with software and hardware artifacts, will find applications in supercomputing, high-speed data storage, connected vehicles, financial fraud mitigation, cyber-security, deep-fake detection, active blast shielding, and hypersonic vehicles. This project will broaden participation in computing by training multiple undergraduate and graduate students through a well-structured research and education plan that leverages existing programs and partnerships at the three partnering universities, including an undergraduate historically black college.This project will demonstrate that complex nonstationary systems can be learned in real-time by integrating modern mathematical tools combined with advances in hardware, notably by generating a field-programmable gate array design for a real-time predictor running on the edge. To that end, customized variations of traditional topological data analysis will be developed to meet the needs of the targeted modeling and forecasting tasks while producing computationally efficient machine learning representations. Concurrently, opportunities and limitations in conducting topological data analysis in real-time and in producing a modular automated programmer for heterogeneous hardware will be identified. Then, software and hardware discoveries will be integrated to demonstrate real-time topological data analysis and to conduct time series modeling and forecasting. Undergraduate students involved in these research projects will be provided with long-term mentored research and learning experiences.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.

高速率系统被定义为经历高速和高振幅事件的动态系统。例子包括高超音速车和主动影响缓解策略。这些机制的高级操作只能通过能够在子毫秒范围内运行的控制和反馈系统来实现，从而需要严格的性能限制。此外，高速系统是高度非线性和非平稳性的，对于传统的实时推理方法，无法提供可信的预测。拓扑数据分析正在对复杂时间序列进行分类广受欢迎。它与架构的机器学习算法的集成在推进高速系统的预测能力方面有希望。但是，拓扑数据分析在计算上是昂贵的，不能在亚毫秒范围内应用。该项目将通过开发和集成数学，软件和硬件基础的进步来研究实时拓扑数据分析功能。该项目的成功完成将产生理论基础，从而使拓扑数据分析与用于建模和预测时间序列的机器学习的整合，从而构成了纯代数拓扑方法的重大飞跃。可以预见的是，开发的基金会以及软件和硬件工件将在超级计算，高速数据存储，互联车辆，经济欺诈缓解，网络安全，深料检测，主动爆炸屏蔽和高音车辆方面找到应用程序。该项目将通过培训多个本科生和研究生培训良好的研究和教育计划来扩大计算的参与，该研究和教育计划利用三所大学的现有计划和合作伙伴关系，包括一所大学历史上的大学黑人学院。该项目将通过在现代数学上进行实时的实时工具来证明，该项目可以证明复杂的非策划系统可以通过实时的范围来实现，这是实时的，该工具可以通过实时的实时工具进行实现，该工具可以实现一致性，以实现的现代数学来实现，以实现范围的实时工具，并且可以通过建立现代化的工具来实现一致性，从而使现代化的工具构成了一项实时的实现，该工具可以实现实时的范围。在边缘运行。为此，将开发传统拓扑数据分析的自定义变化，以满足目标建模和预测任务的需求，同时产生计算高效的机器学习表示。同时，将确定实时进行拓扑数据分析的机会和局限性，并在生产模块化自动化程序员用于异质硬件的过程中。然后，将集成软件和硬件发现以演示实时拓扑数据分析并进行时间序列建模和预测。参与这些研究项目的本科生将提供长期的指导研究和学习经验。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。