公开(公告)号：US20240330699A1

公开(公告)日：2024-10-03

申请号：US18519518

申请日：2023-11-27

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Dezhong YAO ,  Sifan ZHAO ,  Hai JIN

IPC: G06N3/092 ,  G06N20/20

CPC classification number: G06N3/092 ,  G06N20/20

Abstract: A system and method for acceleration of deep-learning computing with edge-terminal collaboration is provided, wherein the system includes at least one terminal device and at least one edge server. The terminal device is configured to, when being present in a service coverage of the at least one edge server, determine an inter-layer partitioning and/or intra-layer partitioning policy for a deep learning model based on first configuration information related to the terminal device itself and second configuration information related to the edge server. And the edge server is configured to execute the inter-layer partitioning and/or intra-layer partitioning policy for the deep learning model in response to an inference request message, so as to implement collaborative inference. In the present disclosure, by using the load-based random forest method to predict the execution time for the DNN model, more accurate prediction results can be obtained.