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Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

Stefano Ambrogio1, Nicola Ciocchini1, Mario Laudato1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET Milano, Italy.

Frontiers in Neuroscience
|March 26, 2016
PubMed
Summary

We developed a novel phase change memory synapse for neuromorphic networks, demonstrating spike-timing dependent plasticity for efficient unsupervised machine learning in smart sensors.

Keywords:
cognitive computingmemristorneural networkneuromorphic circuitspattern recognitionphase change memoryspike timing dependent plasticity

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Neuromorphic networks require efficient synaptic devices for mimicking brain functions.
  • Phase Change Memory (PCM) offers promising characteristics for synaptic applications.

Purpose of the Study:

  • To introduce a novel one-transistor/one-resistor (1T1R) synapse based on PCM.
  • To demonstrate spike-timing dependent plasticity (STDP) in the proposed synapse.
  • To validate the synapse's applicability in neuromorphic networks for pattern recognition.

Main Methods:

  • Fabrication of a 1T1R synapse utilizing PCM technology.
  • Experimental characterization of STDP with varying initial conditions and pulse counts.
  • Simulation of multi-layer (2-3 layers) fully connected neuromorphic networks.

Main Results:

  • The 1T1R synapse successfully exhibits gradual potentiation (crystallization) and depression (amorphization).
  • STDP characteristics were experimentally verified under diverse conditions.
  • Simulations showed high recognition efficiency for visual patterns in the implemented networks.

Conclusions:

  • The proposed 1T1R PCM synapse effectively implements STDP.
  • This technology offers a low-power solution for on-line unsupervised machine learning.
  • The synapse is suitable for smart reconfigurable sensors and neuromorphic computing.