RT-mDL: Supporting Real-Time Mixed Deep Learning Tasks on Edge Platforms

Abstract

Recent years have witnessed an emerging class of real-time applications, e.g., autonomous driving, in which resource-constrained edge platforms need to execute a set of real-time mixed Deep Learning (DL) tasks concurrently. Such an application paradigm poses major challenges due to the huge compute workload of deep neural network models, diverse performance requirements of different tasks, and the lack of real-time support from existing DL frameworks. In this paper, we present RT-mDL, a novel framework to support mixed real-time DL tasks on edge platform with heterogeneous CPU and GPU resource. RT-mDL aims to optimize the mixed DL task execution to meet their diverse real-time/accuracy requirements by exploiting unique compute characteristics of DL tasks. RT-mDL employs a novel storage-bounded model scaling method to generate a series of model variants, and systematically optimizes the DL task execution by joint model variants selection and task priority assignment. To improve the CPU/GPU utilization of mixed DL tasks, RT-mDL also includes a new priority-based scheduler which employs a GPU packing mechanism and executes the CPU/GPU tasks independently. Our implementation on an F1/10 autonomous driving testbed shows that, RT-mDL can enable multiple concurrent DL tasks to achieve satisfactory real-time performance in traffic light detection and sign recognition. Moreover, compared to state-of-the-art baselines, RT-mDL can reduce deadline missing rate by 40.12% while only sacrificing 1.7% model accuracy.