Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

• URL: http://arxiv.org/abs/2307.04339v1
• Date: Mon, 10 Jul 2023 04:30:44 GMT
• Title: Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU
• Authors: Zhihe Zhao, Neiwen Ling, Nan Guan, Guoliang Xing
• Abstract summary: concurrent running of multiple deep neural networks (DNN) Miriam is a contention-aware task coordination framework for multi-DNN inference on edge GPU.
• Score: 7.972518585452826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with varying levels of criticality on edge GPUs remains an area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited and lack hardware-level resource management mechanisms for avoiding resource contention. Therefore, we propose Miriam, a contention-aware task coordination framework for multi-DNN inference on edge GPU. Miriam consolidates two main components, an elastic-kernel generator, and a runtime dynamic kernel coordinator, to support mixed critical DNN inference. To evaluate Miriam, we build a new DNN inference benchmark based on CUDA with diverse representative DNN workloads. Experiments on two edge GPU platforms show that Miriam can increase system throughput by 92% while only incurring less than 10\% latency overhead for critical tasks, compared to state of art baselines.

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