A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

Min Hwi Kim, Sin Hyung Lee, Sungjun Kim, Byung Gook Park

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental reliability characteristics of the resistive-switching memory cell. Also, the superiority of this programming method using the proposed RRAM compact model is demonstrated. In addition, a one weight/one synaptic device structure is newly adopted for realizing high-density synapse arrays by using a nonnegative weight constraint in supervised learning. Finally, the pattern recognition accuracies obtained at the software and hardware levels are compared.

Original languageEnglish
Pages (from-to)37030-37038
Number of pages9
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • Neuromorphic
  • artificial neural network (ANN)
  • cross-point array architecture
  • hardware-driven artificial intelligence
  • resistive-switching random-access memory (RRAM)
  • synaptic device
  • weight transfer

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