Hierarchical highly heterogeneous distributed system based deep learning application optimization framework

Abstract

The present invention discloses a hierarchical highly heterogeneous distributed system based deep learning application optimization framework and relates to the field of deep learning in the direction of computational science. The hierarchical highly heterogeneous distributed system based deep learning application optimization framework comprises a running preparation stage and a running stage. The running preparation stage is used for performing deep neural network training. The running stage performs task assignment to all kinds of devices in the distributed system and uses a data encryption module to perform privacy protection to user sensitive data. Due to heterogeneous characteristics of a system task of the present invention, on the premise that the overall performance is guaranteed, the system response time is reduced, the user experience is guaranteed, the data encryption module based on the neural network can perform privacy protection to user sensitive data at a lower computing cost and storage cost, and the user data security is guaranteed.

Dependencies are inferred from the claim text.