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Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning.

Ui Yeon Won1,2, Quoc An Vu3, Sung Bum Park1

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.

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|May 27, 2023
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Summary
This summary is machine-generated.

This study introduces a novel multi-terminal floating-gate memristor (MT-FGMEM) capable of emulating multi-neuron connections. This artificial neuron significantly reduces energy consumption and achieves high accuracy in unsupervised learning tasks.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Multi-terminal memristors (MT-MEMs) excel at synaptic plasticity but cannot emulate neuronal membrane potentials in multi-neuron networks.
  • Existing MT-MEMs struggle to replicate the complex dynamics of neuronal integration across multiple connections.

Purpose of the Study:

  • To develop a multi-terminal floating-gate memristor (MT-FGMEM) that can emulate multi-neuron connections and neuronal membrane potential.
  • To demonstrate the MT-FGMEM's capability in energy-efficient artificial neuron and synapse implementation.

Main Methods:

  • Utilized graphene's variable Fermi level for charging/discharging the MT-FGMEM via multiple electrodes.
  • Investigated the MT-FGMEM's electrical characteristics, including on/off ratio, retention time, and linear current-voltage behavior.
  • Implemented leaky-integrate-and-fire (LIF) functionality and spike-timing-dependent plasticity (STDP) for neural network emulation.

Main Results:

  • Achieved a high on/off ratio (>10^5) with exceptional retention (~10,000 times longer than other MT-MEMs).
  • Demonstrated linear current-voltage characteristics crucial for accurate spike integration.
  • Created an artificial neuron with drastically reduced energy consumption (150 pJ vs. 11.7 μJ).
  • Successfully emulated spiking neurosynaptic training and directional line classification in a visual cortex model.
  • Achieved 83.08% accuracy in unsupervised learning on the MNIST dataset using the developed artificial neuron and synapse.

Conclusions:

  • The MT-FGMEM effectively mimics temporal and spatial summation in multi-neuron connections, fulfilling LIF functionality.
  • The developed artificial neuron and synapse offer significant energy savings for neuromorphic computing.
  • The MT-FGMEM is a promising candidate for building advanced, energy-efficient neuromorphic computing systems.