Deeply Encoding Stable Patterns From Contaminated Data for Scenery Image Recognition

Highly parallelized memristive binary neural network

At present, in the new hardware design work of deep learning, memristor as a non-volatile memory with computing power has become a research hotspot. The weights in the deep neural network are the floating-point number. Writing a floating-point value into a memristor will result in a loss of accuracy, and the writing process will take more time. The binarized neural network (BNN) binarizes the weights and activation values that were originally floating-point numbers to +1 and -1. This will greatly reduce the storage space consumption and time consumption of programming the resistance value of the memristor. Furthermore, this will help to simplify the programming of memristors in deep neural network circuits and speed up the inference process. This paper provides a complete solution for implementing memristive BNN. Furthermore, we improved the design of the memristor crossbar by converting the input feature map and kernel before performing the convolution operation that can ensure the sign of the input voltage of each port constant. Therefore, we do not need to determine the sign of the input voltage required by the port in advance which simplifies the process of inputting the feature map elements to each port of the crossbar in the form of voltage. At the same time, in order to ensure that the output of the current convolution layer can be directly used as the input of the next layer, we have added a corresponding processing circuit, which integrates batch-normalization and binarization operations.