The growing demand for multi-DNN workloads with unpredictable task arrival times has highlighted the need for interruptible scheduling on edge accelerators. However, existing preemptive frameworks typically assume known task arrival times and rely on CPU-based offline scheduling, which incurs heavy runtime overhead and struggles to handle unpredictable task arrivals. Even worse, prior studies have shown that multi-DNN scheduling requires solving an NP-hard subgraph isomorphism problem on large directed acyclic graphs within limited time, which is extremely challenging. To tackle this, we propose IMMSched, a parallel subgraph isomorphism method that combines Multi-Particle Optimization with the Ullmann algorithm based on a probabilistic continuous-relaxation scheme, eliminating the serial data dependencies of previous works. Finally, a quantized scheduling scheme and a global controller in the hardware architecture further combine multi-particle results for consensus-guided exploration. Evaluations demonstrate that IMMSched achieves orders-of-magnitude reductions in scheduling latency and energy consumption, enabling real-time execution of unpredictable DNN tasks on edge accelerators.

翻译：针对任务到达时间不可预测的多DNN工作负载日益增长的需求，突显了边缘加速器上可中断调度的必要性。然而，现有抢占式框架通常假设已知任务到达时间，并依赖基于CPU的离线调度，这带来了沉重的运行时开销，且难以处理不可预测的任务到达。更糟的是，先前研究表明，多DNN调度需要在有限时间内解决大型有向无环图上的NP难子图同构问题，这极具挑战性。为解决此问题，我们提出IMMSched——一种并行子图同构方法，它将多粒子优化与基于概率连续松弛方案的Ullmann算法相结合，消除了先前工作中串行数据依赖。最后，量化调度方案及硬件架构中的全局控制器进一步融合多粒子结果，实现共识引导的探索。评估表明，IMMSched在调度延迟和能耗上实现了数量级的降低，使得不可预测的DNN任务能在边缘加速器上实时执行。