SHF: Medium: Collaborative Research: Predictive Modeling for Next-generation Heterogeneous System Design

SHF：媒介：协作研究：下一代异构系统设计的预测建模

• 批准号: 1763795
• 负责人: Philip Brisk
• 金额: $ 32.24万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763795&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Predictive

With semiconductor scaling reaching physical limits, performance and power consumption are ever more critical aspects in the design of emerging computer systems. Fast and accurate design models and tools are critical to support future computer system designers in evaluating design options before they can be built. Traditional simulation-based or analytical models are often too slow or inaccurate to effectively support design processes. This project instead develops novel machine learning-based, predictive methodologies to rapidly estimate the performance and power consumption of future generation products at early design stages using observations obtained on commercially available silicon today. Such techniques will allow efficient design cycles ensuring that the next-generation computing infrastructure meets the needs and expectations of consumers and continues to meet them over the product lifecycle. Along with research activities, course material on predictive modeling will be integrated into the university courses taught by the investigators, technology will be transferred to industrial partners through training and tutorials, and tools and models developed in this project will be released as open source software. In addition to training of graduate students, emphasis will be paid to undergraduate student training, towards including federally recognized under-represented groups, training of STEM teachers, and to run summer code camps to increase access for middle school and high school students. This project specifically investigates use of advanced machine learning techniques for prediction of power and performance of any machine based on hardware-dependent and independent application characteristics obtained by running on any existing other machine, focusing on large-scale data center and accelerator technologies, namely multi-core CPUs, GPUs and FPGAs. Specific research tasks include the investigation of: (1) fast and accurate models for system designers and system programmers to perform rapid, early hardware and software design space exploration; (2) fast online prediction models that can be integrated into modern operating systems and virtual machine; and (3) fast yet accurate model training procedures that can create new predictive models while applications run. This research is expected to also allow semiconductor companies to better understand the scenarios under which predictive modeling is sufficiently accurate to be deployed during an industrial design process.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.

随着半导体规模达到物理极限，性能和功耗是新兴计算机系统设计中越来越关键的方面。快速准确的设计模型和工具对于支持未来的计算机系统设计人员在构建之前评估设计方案至关重要。传统的基于仿真的模型或分析模型通常太慢或不准确，无法有效地支持设计过程。相反，该项目开发了基于机器学习的新型预测方法，利用目前商用硅的观察结果，在早期设计阶段快速估计下一代产品的性能和功耗。这些技术将允许有效的设计周期，确保下一代计算基础设施满足消费者的需求和期望，并在产品生命周期内继续满足他们。沿着研究活动，预测建模的课程材料将被整合到研究人员教授的大学课程中，技术将通过培训和教程转让给工业合作伙伴，该项目中开发的工具和模型将作为开源软件发布。除了培训研究生外，还将重点关注本科生培训，包括联邦承认的代表性不足的群体，培训STEM教师，并举办夏令营，以增加初中和高中学生的入学机会。该项目专门研究使用先进的机器学习技术来预测任何机器的功率和性能，基于通过在任何现有的其他机器上运行获得的依赖于硬件和独立的应用程序特性，重点关注大规模数据中心和加速器技术，即多核CPU，GPU和FPGA。具体的研究任务包括：（1）快速和准确的模型，系统设计师和系统程序员执行快速，早期的硬件和软件设计空间探索;（2）快速在线预测模型，可以集成到现代操作系统和虚拟机;（3）快速而准确的模型训练程序，可以在应用程序运行时创建新的预测模型。这项研究还将帮助半导体公司更好地了解预测建模在工业设计过程中部署的准确性。该奖项反映了NSF的法定使命，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。

项目成果

An FPGA-based Programmable Vector Engine for Fast Fully Homomorphic Encryption over the Torus

基于 FPGA 的可编程矢量引擎，用于环面上的快速全同态加密

• 发表时间: 2021
• 期刊: SPSL: Secure and Private Systems for Machine Learning (ISCA Workshop
• 作者: Gener, Serhan;Newton, Parker;Tan, Daniel;Richelson, Silas;Lemieux, Guy;Brisk, Philip
• 通讯作者: Brisk, Philip

Matrix Profile Index Approximation for Streaming Time Series

• DOI: 10.1109/bigdata52589.2021.9671484
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Conference on Big Data (Big Data)
• 作者: Maryam Shahcheraghi;Trevor Cappon;Samet Oymak;E. Papalexakis;Eamonn J. Keogh;Zachary Zimmerman;P. Brisk

Matrix Profile Index Prediction for Streaming Time Series

流时间序列的矩阵轮廓指数预测

• 发表时间: 2020
• 期刊: Workshop on ML for Systems at NeurIPS 2020
• 作者: Shahcheraghi, Maryam;Cappon, Trevor;Oymak, Samet;Papalexakis, Evangelos;Keogh, Eamonn;Zimmerman, Zachary;Brisk, Philip
• 通讯作者: Brisk, Philip

FPGA-based Acceleration of Time Series Similarity Prediction: From Cloud to Edge

• DOI: 10.1145/3555810
• 发表时间: 2022-08
• 期刊: ACM Transactions on Reconfigurable Technology and Systems
• 影响因子: 2.3
• 作者: Amin Kalantar;Zachary Zimmerman;P. Brisk

Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

矩阵配置文件 XVIII：使用学习的近似矩阵配置文件在快速移动的流中进行时间序列挖掘

• DOI: 10.1109/icdm.2019.00104
• 发表时间: 2019
• 期刊: International Conference on Data Mining
• 作者: Zimmerman, Zachary;Shakibay Senobari, Nader;Funning, Gareth;Papalexakis, Evangelos;Oymak, Samet;Brisk, Philip;Keogh, Eamonn
• 通讯作者: Keogh, Eamonn