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

Missouri University Of Science And Technology, Rolla MO

Investigators

Abstract

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.