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

随着半导体规模达到物理极限，性能和功耗在新兴计算机系统的设计中变得越来越关键。快速和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可以构建之前评估设计方案至关重要。传统的基于模拟或分析的模型往往太慢或太不准确，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的预测方法，以利用对当今商业可获得的硅的观察，在早期设计阶段快速估计未来一代产品的性能和功耗。这种技术将允许高效的设计周期，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这些需求和期望。除了研究活动，预测建模的课程材料将被纳入研究人员教授的大学课程，技术将通过培训和教程转移给行业合作伙伴，该项目开发的工具和模型将作为开源软件发布。除了对研究生的培训外，还将重视本科生培训，包括联邦承认的代表性不足群体，培训STEM教师，并举办夏季代码夏令营，以增加初中生和高中生的机会。该项目专门研究使用先进的机器学习技术，根据在任何现有其他机器上运行所获得的依赖于硬件和独立的应用程序特征，预测任何机器的功率和性能，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)快速准确的模型，供系统设计人员和系统程序员进行快速、早期的硬件和软件设计空间探索；(2)可以集成到现代操作系统和虚拟机中的快速在线预测模型；以及(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。预计这项研究还将使半导体公司更好地了解预测建模足够准确的场景，以便在工业设计过程中部署。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。

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.