Design considerations of synaptic device for neuromorphic computing

Shimeng Yu, Duygu Kuzum, H. S Philip Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital Boolean computing. Recently, two-terminal emerging memory devices that show electrically-triggered resistance modulation have been proposed as synaptic devices for neuromorphic computing. The synaptic device candidates include phase change memory (PCM), resistive RAM (RRAM) and conductive bridge RAM (CBRAM), etc. In this paper, we discuss the general design considerations of synaptic devices for plasticity and learning. As a rule of thumb for performance metrics assessment, an ideal synaptic device should have characteristics such as dimension, energy consumption, operation frequency, dynamic range, etc. that are scalable to biological systems with comparable complexity.

Original languageEnglish (US)
Title of host publication2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1062-1065
Number of pages4
ISBN (Print)9781479934324
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 - Melbourne, VIC, Australia
Duration: Jun 1 2014Jun 5 2014

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
Country/TerritoryAustralia
CityMelbourne, VIC
Period6/1/146/5/14

Keywords

  • CBRAM
  • PCM
  • RRAM
  • learning
  • neuromorphic computing
  • plasticity
  • synaptice device

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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