A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation

Shimeng Yu, Bin Gao, Zheng Fang, Hongyu Yu, Jinfeng Kang, H. S.Philip Wong

Research output: Contribution to journalArticlepeer-review

293 Scopus citations

Abstract

Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.

Original languageEnglish (US)
Pages (from-to)1774-1779
Number of pages6
JournalAdvanced Materials
Volume25
Issue number12
DOIs
StatePublished - Mar 25 2013

Keywords

  • artificial visual systems
  • neuromorphic computing
  • oxide RRAM
  • resistive switching
  • synaptic devices

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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