CRII: CSR: NeuroMC---Parallel Online Scheduling of Mixed-Criticality Real-Time Systems via Neural Networks

随着技术的进步，随着晶体管变得越来越小，更多的晶体管可以集成到一个半导体芯片中；这反过来又可以将许多处理器“核心”集成到一个芯片中。新兴的芯片集成了不同类型的处理器核心，这些核心专门用于包括图形处理和模式识别在内的各种功能。多核的可用性给软件程序带来了任务调度问题，即软件的哪些部分应该在哪种类型的核上执行。鉴于各核心的能力不相等，任务可能需要不同的时间才能完成，具体取决于分配给哪个核心。在实时系统中，在将任务映射到核心时，应该满足最后期限，这可能并不总是可能的。此外，错过最后期限的后果并不适用于所有任务。有些人比另一些人更宽容。因此，可以根据任务的关键程度对任务进行广泛的分类。本研究将探讨一种适用于混合临界实时系统的高效调度器。在资源受限的系统中，未能在最后期限前完成任务的后果可能是灾难性的，也可能是轻微的。因此，与错过最后期限相关的处罚各不相同。研究人员计划解决混合临界实时系统的调度问题。该调度问题是NP-Hard问题；所提出的方法涉及基于人工神经网络(NN)的调度器。研究人员将试验并行神经网络以获得更快的收敛速度，并开发一个原型系统来评估该解决方案的效率和可扩展性。该项目预计将带来实时做出近乎最佳的决策的能力。该项目是将并行计算和神经网络结合在一起的更大努力的第一步。该项目的广泛经济和社会影响包括研究与教育的整合，因为它将涉及开发一门新的课程，并重新设计一门关于实时和网络物理系统的现有课程。该项目将让本科生参与图形处理单元的编程，并将寻求支持多名女性学生扩大参与。研究结果、教育材料、软件和实验数据将在项目网站上传播。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 progression of technology, as the transistors become smaller, more of them can be integrated into a semiconductor chip; this in turn enables integration of many processor "cores" into one chip. Emerging chips integrate different types of processor cores, which are specialized for various functions including graphics processing and pattern recognition. Availability of many cores creates a task scheduling problem for a software program, namely which portions of the software should execute on what type of core. Given that the capabilities of the cores are not equal, a task may take different amounts of time to finish depending on which core it was assigned to. In a real-time system, while mapping tasks to cores, deadlines should be met, which may not always be possible. Further, the consequences of missing a deadline are not the same for all tasks. Some are more forgiving than others. Consequently, tasks can be classified broadly based on their criticality. This research will investigate an efficient scheduler for mixed-criticality real-time systems. In a resource constrained system, consequences of failure to meet a deadline may range from catastrophic to Minor. Consequently, there are varying penalties associated with missing deadlines. The investigator plans to address the scheduling problem of mixed-criticality real-time systems. This scheduling problem is NP-hard; the proposed approach involves an artificial neural network (NN)-based scheduler. The investigator will experiment with parallel NNs for faster convergence, and develop a prototype system to evaluate the efficiency and scalability of this solution. The project is expected to lead to the ability to make near-optimal decisions in real time. This project serves as the initial step of a larger effort in bringing the parallel computation and neural networks together. Broad economic and societal impacts of this project include integration of research and education, as it will involve development of a new course offering and redesign of an existing course on real-time and cyber-physical systems. The project will involve undergraduate students in programming graphics processing units and will seek to support multiple female students to broaden participation. Research results, educational material, software, and experimental data will be disseminated on the project website.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.