DEVICE AND METHOD FOR CONTROLLING A ROBOT BASED ON A PRUNED MODEL

原文（中国語）

An apparatus for controlling a robot comprises a processor and memory storing instructions, which, when executed by the processor, cause the apparatus to determine weighting values for multiple layers in a pre-trained deep learning model based on similarities among channels within each layer. Each layer is assigned a respective weighting value. The apparatus further determines a first pruning rate for all layers based on the inference time of the model, reflecting the time required for input processing and output prediction. A second pruning rate for each layer is determined by multiplying the first pruning rate by the corresponding weighting value, followed by random channel removal at this rate to prune the layers. The apparatus then outputs a signal based on the pruned layers and uses this signal to control the robot.