Abstract: A processing apparatus for a neural network calculation and an operating method thereof are proposed. The processing apparatus for a neural network calculation according to an exemplary embodiment includes a plurality of processor units which performs the learning of a neural network, a plurality of weight memories provided to store a weight corresponding to each of a plurality of layers which forms the neural network, and a main memory which acquires and stores the weight updated from the plurality of weight memories when the learning is completed.

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.