Hybrid CMOS-Memristive Convolutional computation for on-chip learning

Yang Zhang, Menglin Cui, Yang Liu, Linlin Shen

Research output: Journal PublicationArticlepeer-review

9 Citations (Scopus)

Abstract

Nanoscale memristive devices have elicited widespread interests in implementing compact and energy-efficient neuromorphic computing systems. In this paper, a learning system of hybrid CMOS-memristive convolutional computation for on-chip learning is presented. Two different methods to achieve the convolutional computation on-chip are provided. One method is to utilize a one memristor (1M)-based array to realize both image convolution and recognition. Another method is to perform the convolutional computation on-chip through a hybrid CMOS-memristive learning circuits. In addition, a modified back propagation (BP) algorithm suitable for the proposed memristive neural networks is applied to perform image convolution and recognition. Another highlight of the proposed method is that the presented hybrid CMOS-memristive neural networks can be expanded to deep convolutional neural networks (DNN).

Original languageEnglish
Pages (from-to)48-56
Number of pages9
JournalNeurocomputing
Volume355
DOIs
Publication statusPublished - 25 Aug 2019
Externally publishedYes

Keywords

  • Convolutional neural networks
  • Crossbar array
  • Image recognition
  • Memristor

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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