Demonstration of Convolution Kernel Operation on Resistive Cross-Point Array

Abstract

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 $20 \times 20$ pixels of black-and-white MNIST handwritten digits. The experiments were performed on the fabricated $12 \times 12$ 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.