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/HfOx/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.

Original languageEnglish (US)
Article number7479502
Pages (from-to)870-873
Number of pages4
JournalIEEE Electron Device Letters
Issue number7
StatePublished - Jul 2016


  • Convolution kernel
  • cross-point array
  • neuromorphic computing
  • resistive memory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


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