Presentation
Hierarchical Pareto-Prompted Optimization for Probabilistic DAG Scheduling on Real-Time Systems
DescriptionReal-time automotive workloads modeled as directed acyclic graphs (DAGs) with probabilistic execution times face severe latency bottlenecks, where shared tasks couple multiple sensor-to-actuator paths.
Minimizing end-to-end latency under such variability requires navigating a complex joint space of task mappings and offsets. To address this, we propose a Pareto-prompted metaheuristic with hierarchical mapping-then-offset refinement, utilizing Pareto dominance to search for non-dominated schedules.
Evaluated on production navigation-on-autopilot workloads running on NVIDIA Orin-N–based vehicles, our method satisfies all constraints and significantly outperforms an industrial static baseline, reducing mean latency by up to 12% and 99th-percentile latency by up to 15%.
Minimizing end-to-end latency under such variability requires navigating a complex joint space of task mappings and offsets. To address this, we propose a Pareto-prompted metaheuristic with hierarchical mapping-then-offset refinement, utilizing Pareto dominance to search for non-dominated schedules.
Evaluated on production navigation-on-autopilot workloads running on NVIDIA Orin-N–based vehicles, our method satisfies all constraints and significantly outperforms an industrial static baseline, reducing mean latency by up to 12% and 99th-percentile latency by up to 15%.
Event Type
Research Manuscript
TimeMonday, July 271:30pm - 1:42pm PDT
LocationMtg Room 203C