Kernel Application of the Stacked Crossbar Array Composed of Self‐Rectifying Resistive Switching Memory for Convolutional Neural Networks

Abstract

Herein, a feasible method is provided for circuit implementation of the convolutional neural network (CNN) in neuromorphic hardware using the multiple layers‐stacked resistance switching random access memory (ReRAM). The specific ReRAM is accompanied by self‐rectification functionality. The single‐input multiple‐output (SIMO) scheme is an optimum method in the extraction of the features of a letter with a versatile selection of the intended features, whereas the multiple‐input single‐output (MISO) scheme provides a highly efficient method to extract the features from the color image, which is composed of several component color images. The Pt/HfO2−x/TiN‐based self‐rectification ReRAM that is integrated into the sidewalls of the two‐layer structure provides a sound framework for the circuit implementation of the SIMO and MISO schemes. The appropriate selection of the kernels for image compression and feature extraction greatly facilitates the CNN in neuromorphic hardware.

Keywords

• convolutional neural networks
• hafnium oxide
• kernels
• self-rectifying resistive switching random access memory
• stacked crossbar arrays