Lightweight multi-dimensional memristive CapsNet

Capsule network (CapsNet) is a novel neural network architecture that overcame the drawback of loss of poses and position caused in convolutional neural networks and achieves better results than convolutional neural networks in some tasks. However, CapsNet computing efficiency needs to be improved. This paper uses the lightweight network method to design the structure of the capsule network reconstruction layer and proposes a lightweight capsule network, DSC-CapsNet, which can effectively improve network computing efficiency and keep network performance. Moreover, the memristor-based circuit structure corresponding to the main operation part of DSC-CapsNet was developed to enable a high-speed method of inference. It provides a modern approach to hardware deployment in terminal applications.