ImageSplit: Modular Memristive Neural Network

In this paper, we present an input regulated modular neural network architecture for realising large neural networks. The proposed network become scalable in design for hardware by splitting an input image into non-overlapping blocks to be processed individually by small sized neural network blocks. Classification is done by fusing the class decisions detected from each individual block. We test this approach on three different datasets using various deep neural network architectures. The analog computation of the proposed splitting technique were evaluated using memristive-crossbar neural network architectures in SPICE tool. The results obtained show that splitting and processing an image in multiple small sized network gives higher accuracy as compared to processing the image as a whole in a larger single network. The area and power requirements of the neural network hardware architecture with the proposed splitting technique was computed and compared with the non-splitting case.