Hard and Soft Real-Time DNN Inference Scheduling on Heterogeneous Multi-Accelerator Systems via Reinforcement Learning

We present a scheduling framework for heterogeneous multi-accelerator systems executing both hard real-time and soft real-time QoS-aware DNN inference workloads. The framework combines an online reinforcement learning (RL) scheduler with a deterministic runtime safety layer. The RL scheduler dynamically generates candidate schedules by jointly selecting sub-job priorities and accelerator assignments, while runtime certification guarantees that all HRT deadlines remain satisfied independently of the learned policy. To enable runtime certification in heterogeneous precedence-constrained systems, we introduce a schedulability-preserving prefix replacement criterion based on machine-wise cuts. The proposed criterion guarantees that runtime-generated schedule prefixes preserve the existence of a feasible continuation without requiring complete finish-time dominance with respect to a feasible reference schedule. Experimental results on heterogeneous DNN accelerator platforms show that the proposed framework preserves HRT schedulability while improving QoS-aware deadline satisfaction, client-level service quality, and accelerator utilization compared to state-of-the-art scheduling approaches.