Device and materials requirements for neuromorphic computing

Raisul Islam, Haitong Li, Pai Yu Chen, Weier Wan, Hong Yu Chen, Bin Gao, Huaqiang Wu, Krishna Saraswat, H. S. Philip Wong

Research output: Contribution to journalReview articlepeer-review

106 Scopus citations


Energy efficient hardware implementation of artificial neural network is challenging due the 'memory-wall' bottleneck. Neuromorphic computing promises to address this challenge by eliminating data movement to and from off-chip memory devices. Emerging non-volatile memory (NVM) devices that exhibit gradual changes in resistivity are a key enabler of in-memory computing - a type of neuromorphic computing. In this paper, we present a review of some of the NVM devices (RRAM, CBRAM, PCM) commonly used in neuromorphic application. The review focuses on the trade-off between device parameters such as retention, endurance, device-to-device variation, speed and resistance levels, and the interplay with target applications. This work aims at providing guidance for finding the optimized resistive memory devices material stack suitable for neuromorphic application.

Original languageEnglish (US)
Article number113001
JournalJournal of Physics D: Applied Physics
Issue number11
StatePublished - Jan 18 2019


  • deep neural network
  • neuromorphic computing
  • non volatile memory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Acoustics and Ultrasonics
  • Surfaces, Coatings and Films


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