Abstract: Disclosed are a task scheduling processing method, an electronic device, and a storage medium. The method includes: obtaining to-be-orchestrated task information and target resource information; determining a first quantity of threads based on the target resource information; determining an orchestration period based on the to-be-orchestrated task information; determining, based on the orchestration period, time slices respectively corresponding to the first quantity of threads; generating a task scheduling table based on the to-be-orchestrated task information and the time slices respectively corresponding to all the threads, the task scheduling table including threads respectively corresponding to all to-be-orchestrated tasks and corresponding execution time information in the time slice for the thread; and scheduling all the to-be-orchestrated tasks based on the task scheduling table.

Abstract: Disclosed are a method and apparatus for accelerating inference of a neural network model, an electronic device, and a medium. The method includes: acquiring image training data, text training data, or speech training data; determining a first neural network model to be accelerated; converting a preset operation on a preset network layer in the first neural network model to a first operation for simulating operational logic of a target operation to obtain a second neural network model; performing, based on the image training data, the text training data, or the speech training data, quantization aware training on the second neural network model by a preset bit width to obtain a third neural network model which is quantized; and converting the first operation of the third neural network model to the target operation, to obtain a target neural network model, which is accelerated, corresponding to the first neural network model.