Neuron inspired data encoding memristive multi-level memory cell

Aidana Irmanova, Alex Pappachen James

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalAnalog Integrated Circuits and Signal Processing
DOIs
Publication statusAccepted/In press - Mar 19 2018

Keywords

  • Memristors
  • Multi-level memory
  • Neuromorphic computing
  • Ternary logic

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Surfaces, Coatings and Films

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