Phase transition oxide neuron for spiking neural networks

Matthew Jerry, Wei Yu Tsai, Baihua Xie, Xueqing Li, Vijay Narayanan, Arijit Raychowdhury, Suman Datta

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

18 Scopus citations


Spiking neural networks are expected to play a vital role in realizing ultra-low power hardware for computer vision applications [1]. While the algorithmic efficiency is promising, their solid-state implementation with traditional CMOS transistors lead to area expensive solutions. Transistors are typically designed and optimized to perform as switches and do not naturally exhibit the dynamical properties of neurons. In this work, we harness the abrupt insulator-to-metal transition (IMT) in a prototypical IMT material, vanadium dioxide (VO2) [2], to experimentally demonstrate a compact integrate and fire spiking neuron [3]. Further, we show multiple spiking dynamics of the neuron relevant to implementing 'winner take all' max pooling layers employed in image processing pipelines.

Original languageEnglish (US)
Title of host publication74th Annual Device Research Conference, DRC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509028276
StatePublished - Aug 22 2016
Event74th Annual Device Research Conference, DRC 2016 - Newark, United States
Duration: Jun 19 2016Jun 22 2016

Publication series

NameDevice Research Conference - Conference Digest, DRC
ISSN (Print)1548-3770


Other74th Annual Device Research Conference, DRC 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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