Apparatus and method for optimizing artificial intelligence model loading in embedded environment

Abstract

The invention discloses a loading optimization device and method for artificial intelligence models in an embedded environment. According to an embodiment of the present invention, the loading optimization method for artificial intelligence models in an embedded environment includes steps such as acquiring model information for the target model based on artificial intelligence, defining multiple partition scenarios for splitting the target model into multiple blocks based on the model information, and considering memory information of a computing device for executing the target model and computational workload information associated with the target model to explore the optimal scenario among the multiple partition scenarios through a reinforcement learning-based loading optimization model.

Dependencies are inferred from the claim text.