The widespread use of Deep Neural Networks (DNNs) is limited by high computational demands, especially in constrained environments. GPUs, though effective accelerators, often face underutilization and rely on coarse-grained scheduling. This paper introduces DARIS, a priority-based real-time DNN scheduler for GPUs, utilizing NVIDIA’s MPS and CUDA streaming for spatial sharing, and a synchronization-based staging method for temporal partitioning. In particular, DARIS improves GPU utilization and uniquely analyzes GPU concurrency by oversubscribing computing resources. It also supports zero-delay DNN migration between GPU partitions. Experiments show DARIS improves throughput by $15 \%$ and $11.5 \%$ over batching and state-of-the-art schedulers, respectively, even without batching. All high-priority tasks meet deadlines, with low-priority tasks having under 2% deadline miss rate. High-priority response times are 33% better than those of low-priority tasks.

Deep Neural Networks (DNNs) are useful in many applications, including transportation, healthcare, and speech recognition. Despite various efforts to improve accuracy, few works have studied DNN in the context of real-time requirements. Coarse resource allocation and sequential execution in existing frameworks result in underutilization. In this work, we conduct GPU speedup gain analysis and propose SGPRS, the first real-time GPU scheduler considering zero configuration partition switch. The proposed scheduler not only meets more deadlines for parallel tasks but also sustains overall performance beyond the pivot point.