Accelerating automatic model finding with layer replications case study of MobileNetV2

In this paper, we propose a method to reduce the model architecture searching time. We consider MobileNetV2 for 3D face recognition tasks as a case study and introducing the layer replication to enhance accuracy. For a given network, various layers can be replicated, and effective replication can yield better accuracy. Our proposed algorithm identifies the optimal layer replication configuration for the model. We considered two acceleration methods: distributed data-parallel training and concurrent model training. Our experiments demonstrate the effectiveness of the automatic model finding process for layer replication, using both distributed data-parallel and concurrent training under different conditions. The accuracy of our model improved by up to 6% compared to the previous work on 3D MobileNetV2, and by 8% compared to the vanilla MobileNetV2. Training models with distributed data-parallel across four GPUs reduced model training time by up to 75% compared to traditional training on a single GPU. Additionally, the automatic model finding process with concurrent training was 1,932 minutes faster than the distributed training approach in finding an optimal solution.