Neuromorphic Computing Based on Emerging Memory Technologies

Bipin Rajendran, Fabien Alibart

Research output: Contribution to journalReview articlepeer-review

52 Scopus citations

Abstract

In this paper, we review some of the novel emerging memory technologies and how they can enable energy-efficient implementation of large neuromorphic computing systems. We will highlight some of the key aspects of biological computation that are being mimicked in these novel nanoscale devices, and discuss various strategies employed to implement them efficiently. Though large scale learning systems have not been implemented using these devices yet, we will discuss the ideal specifications and metrics to be satisfied by these devices based on theoretical estimations and simulations. We also outline the emerging trends and challenges in the path towards successful implementations of large learning systems that could be ubiquitously deployed for a wide variety of cognitive computing tasks.

Original languageEnglish (US)
Article number7422838
Pages (from-to)198-211
Number of pages14
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume6
Issue number2
DOIs
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Cognitive computing
  • memristor
  • neuromorphic engineering
  • phase change memory (PCM)
  • resistive random-Access memory (RRAM)
  • spin-Transfer torque random-Access memory (STT-RAM)

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