An ultra-compact and low power neuron based on SOI platform

V. Ostwal, R. Meshram, B. Rajendran, U. Ganguly

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

12 Scopus citations

Abstract

Analog and digital circuit designs have been proposed to mimic the biological neuron in CMOS compatible learning circuits for 'brain' like computing. However, the adaptation of such conventional circuit based strategies requires many devices, large areas and hence power consumption. We propose a neuronal device based on the well-investigated impact-ionization based NPN selector on an SOI platform. The neuronal device has a small footprint (225∗F2) and low active power (11.5nW/spike) and provides ∼10,000x speed-up over biological timescales. In comparison to analog neuron, ultra-high density (>60x improvement) and low power operation (>5x improvement) are demonstrated.

Original languageEnglish (US)
Title of host publication2015 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479973750
DOIs
StatePublished - Jun 3 2015
Externally publishedYes
Event2015 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2015 - Hsinchu, Taiwan, Province of China
Duration: Apr 27 2015Apr 29 2015

Publication series

NameInternational Symposium on VLSI Technology, Systems, and Applications, Proceedings
Volume2015-June
ISSN (Print)1930-8868

Other

Other2015 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2015
Country/TerritoryTaiwan, Province of China
CityHsinchu
Period4/27/154/29/15

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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