POS: An Operator Scheduling Framework for Multi-model Inference on Edge Intelligent Computing

Edge intelligent applications, such as autonomous driving usually deploy multiple inference models on resource-constrained edge devices to execute a diverse range of concurrent tasks, given large amounts of input data. One challenge is that these tasks need to produce reliable inference results simultaneously with millisecond-level latency to achieve real-time performance and high quality of service (QoS). However, most of the existing deep learning frameworks only focus on optimizing a single inference model on an edge device. To accelerate multi-model inference on a resource-constrained edge device, in this paper we propose POS, a novel operator-level scheduling framework that combines four operator scheduling strategies. The key to POS is a maximum entropy reinforcement learning-based operator scheduling algorithm MEOS, which generates an optimal schedule automatically. Extensive experiments show that POS outperforms five state-of-the-art inference frameworks: TensorFlow, PyTorch, TensorRT, TVM, and IOS, by up to 1.2x similar to 3.9x inference speedup consistently, with 40% improvement on GPU utilization. Meanwhile, MEOS reduces the scheduling overhead by 37% on average, compared to five baseline methods including sequential execution, dynamic programming, greedy scheduling, actor-critic, and coordinate descent search algorithms.