The present invention discloses a deep learning offline inference load balancing method for Internet of Vehicles. The method comprises: determining a main vehicle in the Internet of Vehicles; calculating a memory footprint of each layer of a deep learning neural network to obtain a memory consumption sequence; smoothing the memory consumption sequence; calculating a fluctuation feature sequence of a neural network layer; performing task division on the deep learning neural network to obtain an initial task sequence; obtaining computing performance of each node device in the Internet of Vehicles; obtaining a redundant multiple sequence of computing performance of all nodes of the Internet of Vehicles and performing correction on the initial task sequence with the lowest computing performance as a benchmark; and obtaining optimal matching between tasks in the corrected task is sequence and vehicle nodes, and allocating the tasks to the respective vehicle nodes. The present invention can be used to realize cross-device collaboration to complete tasks in the scene of the Internet of Vehicles with low design cost and high practicability.
Deep learning offline inference load balancing method for Internet of Vehicles