Modularizing while Training: A New Paradigm for Modularizing DNN Models

Abstract

Deep neural network (DNN) models have become increasingly crucial components of intelligent software systems. However, training a DNN model is typically expensive in terms of both time and computational resources. To address this issue, recent research has focused on reusing existing DNN models - borrowing the concept of software reuse in software engineering. However, reusing an entire model could cause extra overhead or inherit the weaknesses from the undesired functionalities. Hence, existing work proposes to decompose an already trained model into modules, i.e., modularizing-after-training, to enable module reuse. Since the trained models are not built for modularization, modularizing-after-training may incur huge overhead and model accuracy loss. In this paper, we propose a novel approach that incorporates modularization into the model training process, i.e., modularizing-while-training (MwT). We train a model to be structurally modular through two loss functions that optimize intra-module cohesion and inter-module coupling. We have implemented the proposed approach for modularizing Convolutional Neural Network (CNN) models. The evaluation results on representative models demonstrate that MwT outperforms the existing state-of-the-art modularizing-after-training approach. Specifically, the accuracy loss caused by MwT is only 1.13 percentage points, which is less than that of the existing approach. The kernel retention rate of the modules generated by MwT is only 14.58%, with a reduction of 74.31% over the existing approach. Furthermore, the total time cost required for training and modularizing is only 108 minutes, which is half the time required by the existing approach. Our work demonstrates that MwT is a new and more effective paradigm for realizing DNN model modularization, offering a fresh perspective on achieving model reuse.