Automatic Design of Convolutional Neural Network Architectures Under Resource Constraints

With the rise of various smart electronics and mobile/edge devices, many existing high-accuracy convolutional neural network (CNN) models are difficult to be applied in practice due to the limited resources, such as memory capacity, power consumption, and spectral efficiency. In order to meet these constraints, researchers have carefully designed some lightweight networks. Meanwhile, to reduce the reliance on manual design on expert experience, some researchers also work to improve neural architecture search (NAS) algorithms to automatically design small networks, exploiting the multiobjective approaches that consider both accuracy and other important goals during optimization. However, simply searching for smaller network models is not consistent with the current research belief of ``the deeper the better'' and may affect the effectiveness of the model and thus waste the limited resources available. In this article, we propose an automatic method for designing CNNs architectures under constraint handling, which can search for optimal network models meeting the preset constraint. Specifically, an adaptive penalty algorithm is used for fitness evaluation, and a selective repair operation is developed for infeasible individuals to search for feasible CNN architectures. As a case study, we set the complexity (the number of parameters) as a resource constraint and perform multiple experiments on CIFAR-10 and CIFAR-100, to demonstrate the effectiveness of the proposed method. In addition, the proposed algorithm is compared with a state-of-the-art algorithm, NSGA-Net, and several manual-designed models. The experimental results show that the proposed algorithm can successfully solve the problem of the uncertain size of the optimal CNN model under the random search strategy, and the automatically designed CNN model can satisfy the predefined resource constraint while achieving better accuracy.