AND Flash Array Based on Charge Trap Flash for Implementation of Convolutional Neural Networks

Hyun Seok Choi, Hyungjin Kim, Jong Ho Lee, Byung Gook Park, Yoon Kim

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Various memory devices have been proposed for implementing synapse devices in neuromorphic systems. In this letter, an AND flash array based on charge trap flash (CTF) memory was proposed. CTF-based synapse devices are particularly suitable for off-chip learning applications because they have excellent reliability and stable multi-level operation characteristics. In addition, we proposed a method to implement convolutional neural networks in the proposed array, and performed system-level simulation using the characteristics of the fabricated device. Finally, we investigated the accuracy degradation of the neuromorphic system related to data retention and proposed a multiple cell mapping scheme to address this degradation issue.

Original languageEnglish
Article number9201492
Pages (from-to)1653-1656
Number of pages4
JournalIEEE Electron Device Letters
Volume41
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Synapse device
  • charge trap flash
  • convolutional neural network
  • neuromorphic system
  • synapse array

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