The application discloses a multi-task scheduling method of a heterogeneous processor and belongs to the computer field. In the case that the storage resource of an end side is insufficient, the user cannot use a
single model to parallel infer different frame data, and the existing resources are reused as much as possible to improve the
inference efficiency in the scene that the camera data / frame
data needs to be processed in parallel. Meanwhile, different priorities are set for different
inference tasks and different
inference tasks of the same model,
multiple models exist in
cascade, and the multiple model cascades return in the middle, and the multi-task scheduling method is provided, which can effectively improve the efficiency of the end side inference task and significantly reduce the waste of the computing resource.