Enabling Scalable Real-Time Certification for AI-Oriented Safety-Critical Systems

Funded by NSF Cyber-Physical Systems Program.

PI: Jim Anderson. Co-PIs: Don Smith, Ron Alterovitz, and Prakash Sarathy (Northrop Grumman).

The Challenge.

In avionics, an evolution is underway to endow aircraft with "thinking" capabilities through the use of artificial-intelligence (AI) techniques. This evolution is being fueled by the availability of high-performance embedded hardware platforms, typically in the form of multicore machines augmented with accelerators that can speed up certain kinds of computations. Unfortunately, avionics software certification processes, which are rooted in the twin concepts of time and space partitioning, have not kept pace with this evolution. Broadly speaking, "time partitioning" means that the real-time constraints (usually deadline requirements) of each system component can be certified independently, and "space partitioning" means that memory accesses by one component cannot adversely affect other components. On a uniprocessor, these concepts can be simply applied to decompose a system into smaller components that can be specified, implemented, and understood separately. On a multicore+accelerator platform, however, component isolation is much more difficult to achieve efficiently. This fact points to a looming dilemma: unless reasonable notions of component isolation can be provided in this context, certifying AI-based avionics systems will likely be impractical.

The Approach.

This project is addressing this dilemma through multi-faceted research involving real-time systems, safety, autonomy, and embedded system architectures. A central focus of this effort is the development of a framework to support components on multicore+accelerator platforms. This framework specifically targets AI-based avionics use cases that must pass real-time certification. It is being designed to balance the need to isolate components in time and space with the need for efficient execution. In allocating computing resources to components in this framework, execution time bounds are needed for the individual programs within each component. New timing-analysis methods are being developed for obtaining these bounds at different safety levels. Research is also being conducted on performance/timeliness/accuracy tradeoffs that arise when refactoring time-limited AI computations for perception, planning, and control into components.

Significance.

There has been a continuous push over the past 40 years toward more semi-autonomous and autonomous functions in avionics. This push began with auto-pilot functions and is increasingly being fueled by advances in AI software. Avionics certification procedures have not kept pace with these advances. This project is focusing on a key aspect of certification: validating real-time correctness. When finished, the framework being developed in this project will be made available to the world at large through open-source software. This software will include operating-system extensions for supporting components in an isolated way and mechanisms for forming components and assessing their timing correctness.


Publications


T. Amert, Enabling Real-Time Certification of Autonomous Driving Applications, Ph.D. Dissertation, Department of Computer Science, The University of North Carolina at Chapel Hill, August 2021. PDF . Winner, 2022 ACM SIGBED Paul Caspi Memorial Dissertation Award.


N. Otterness, Developing Real-Time GPU-Sharing Platforms for Artificial-Intelligence Applications, Ph.D. Dissertation, Department of Computer Science, The University of North Carolina at Chapel Hill, August 2022. PDF .


S. Osborne, Using Simultaneous Multithreading to Support Real-Time Scheduling, Ph.D. Dissertation, Department of Computer Science, The University of North Carolina at Chapel Hill, August 2023. PDF .


S. Voronov, Scheduling Real-Time Graph-Based Workloads, Ph.D. Dissertation, Department of Computer Science, The University of North Carolina at Chapel Hill, August 2023. PDF .


R. Wagle, Z. Tong, R. Sites, and J. Anderson, " Want Predictable GPU Execution? Beware SMIs!", Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, December 2023, to appear. PDF .


Z. Tong, S. Ahmed, and J. Anderson, " Holistically Budgeting Processing Graphs", Proceedings of the 44th IEEE Real-Time Systems Symposium, pp. 27–39, December 2023. PDF .


S. Ahmed and J. Anderson, " Soft Real-Time Gang Scheduling", Proceedings of the 44th IEEE Real-Time Systems Symposium, pp. 331-343, December 2023. PDF .


J. Goh and J. Anderson, " Reducing Response-Time Bounds via Global Fixed Preemption Point EDF-like Scheduling", Proceedings of the 29th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 117-126, August 2023. PDF .


S. Ahmed and J. Anderson, " Optimal Multiprocessor Locking Protocols under FIFO Scheduling", Proceedings of the 35th Euromicro Conference on Real-Time Systems, pp. 16.1-16.21, July 2023. PDF .


J. Bakita and J. Anderson, " Hardware Compute Partitioning on NVIDIA GPUs", Proceedings of the 29th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 54–66, May 2023. Winner, outstanding paper award. pp. 16.1-16.21, July 2023. PDF .


N. Otterness and J. Anderson, " Exploring AMD GPU Scheduling Details by Experimenting With `Worst Practices'", Real-Time Systems, special issue of outstanding papers from the 29th International Conference on Real-Time Networks and Systems (RTNS 2021), Volume 58, pp. 105–133, March 2022. PDF .


T. Amert, M. Yang, S. Nandi, T. Vu, J. Anderson, and F.D. Smith, " The Price of Schedulability in Cyclic Workloads: The History-vs.-Response-Time-vs.-Accuracy Trade-Off", Journal of Systems Architecture, ispecial issue of outstanding papers from the 23rd IEEE International Symposium on Real-Time Distributed Computing (ISORC 2020), Volume 120, November 2021. PDF .


T. Yandrofski, J. Chen, N. Otterness, J. Anderson, and F.D. Smith, " Making Powerful Enemies on NVIDIA GPUs", Proceedings of the 43rd IEEE Real-Time Systems Symposium, pp. 383–395, December 2022. PDF .


J. Bakita and J. Anderson, " Enabling GPU Memory Oversubscription via Transparent Paging to an NVMe SSD", Proceedings of the 43rd IEEE Real-Time Systems Symposium, pp. 370–382, December 2022. PDF .


T. Amert, Z. Tong, S. Voronov, J. Bakita, F.D. Smith, and J. Anderson, " TimeWall: Enabling Time Partitioning for Real-Time Multicore+Accelerator Platforms", Proceedings of the 42nd IEEE Real-Time Systems Symposium, pp. 455-468, December 2021. PDF .


S. Voronov, S. Tang, T. Amert, and J. Anderson, " AI Meets Real-Time: Addressing Real-World Complexities in Graph Response-Time Analysis", Proceedings of the 42nd IEEE Real-Time Systems Symposium, pp. 82–96, December 2021. PDF .


T. Amert and J. Anderson, " CUPiDRT: Detecting Improper GPU Usage in Real-Time Applications", Proceedings of the 24th IEEE International Symposium on Real-Time Distributed Computing, pp. 86-95, June 2021. PDF .


N. Otterness and J. Anderson, " Exploring AMD GPU Scheduling Details by Experimenting With `Worst Practices'", Proceedings of the 29th International Conference on Real-Time Networks and Systems , pp. 24-34, April 2021. PDF . Winner, best paper award.


Other papers that acknowledge this grant can be found on the PI's Publications Page .


Last modified 20 December 2023