  With the fast development of deep neural networks (DNNs), many real-world
applications are adopting multiple models to conduct compound tasks, such as
co-running classification, detection, and segmentation models on autonomous
vehicles. Such multi-tenant DNN inference cases greatly exacerbate the
computational complexity and call for comprehensive collaboration for
graph-level operator scheduling, runtime-level resource awareness, as well as
hardware scheduler support. However, the current scheduling support for such
multi-tenant inference is still relatively backward. In this work, we propose a
resource-aware scheduling framework for efficient multi-tenant DNN inference on
GPU, which automatically coordinates DNN computing in different execution
levels. Leveraging the unified scheduling intermediate representation and the
automated ML-based searching algorithm, optimal schedules could be generated to
wisely adjust model concurrency and interleave DNN model operators, maintaining
a continuously balanced resource utilization across the entire inference
process, and eventually improving the runtime efficiency. Experiments show that
we could consistently achieve 1.3-1.7x speed-up, compared to regular DNN
runtime libraries (e.g., CuDNN, TVM) and particular concurrent scheduling
methods (e.g., NVIDIA Multi-Stream).
