Demonstration of Convolution Kernel Operation on Resistive Cross-Point Array

Convolution is the key operation in the convolutional neural network, one of the most popular deep learning algorithms. The implementation of the convolution kernel on the resistive cross-point array is different than the implementation of the matrix-vector multiplication in prior works. In this letter, we propose a dimensional reduction of 2-D kernel matrix into 1-D column vector, i.e., a column of the array, and enable the parallel readout of multiple 2-D kernels simultaneously. As a proof-of-concept demonstration, we use the Prewitt kernels to detect both horizontal and vertical edges of the 20 × 20 pixels of black-and-white MNIST handwritten digits. The experiments were performed on the fabricated 12 × 12 resistive cross-point array based on the Pt/HfO_x/TiN structure. The experimental results of the Prewitt kernel operation perfectly matches the simulation results, indicating the feasibility of the proposed implementation methodology of the convolution kernel on resistive cross-point array.