A volatile RRAM synapse for neuromorphic computing

E. Covi*, D. Ielmini, Y. H. Lin, W. Wang, T. Stecconi, V. Milo, A. Bricalli, E. Ambrosi, G. Pedretti, T. Y. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Neuromorphic computing has emerged as a promising approach for autonomous systems able to learn, adapt, and interact in real time with the environment. To build neuromorphic hardware, the recent development of novel material-based devices such as resistive switching memory (RRAM) has shown to be crucial since this class of devices offers the unique advantage to implement neuron and synaptic functions in silico by device physics, thus avoiding bulky circuits and very complex algorithms. In this work, we first explore volatile switching behaviour of RRAM devices, investigating their ability to capture short-term plasticity (STP) and short-term memory (STM) functionalities. Then, we characterise a volatile RRAM synapse, discussing its potential use in a spiking neural network for speech recognition applications.

Original languageEnglish
Title of host publication2019 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages903-906
Number of pages4
ISBN (Electronic)9781728109961
DOIs
StatePublished - Nov 2019
Event26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019 - Genoa, Italy
Duration: 27 Nov 201929 Nov 2019

Publication series

Name2019 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019

Conference

Conference26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019
Country/TerritoryItaly
CityGenoa
Period27/11/1929/11/19

Keywords

  • Resistive switching memory (RRAM)
  • Short-term memory (STM)
  • Short-term plasticity (STP)
  • Volatile switching

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