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Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system.

Hyungjin Kim1, Sungmin Hwang1, Jungjin Park1

  • 1Inter-university Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea.

Nanotechnology
|August 19, 2017
PubMed
Summary

This study introduces a silicon synaptic transistor for efficient brain-inspired computing. The research demonstrates that inhibitory synapses are crucial for effective pattern recognition in hardware neural networks.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • Neuromorphic systems offer power-efficient computing paradigms.
  • Silicon synaptic transistors are key components for hardware neural networks.

Purpose of the Study:

  • To develop a silicon synaptic transistor for hardware neural networks.
  • To analyze spike-timing dependent plasticity (STDP).
  • To demonstrate pattern recognition capabilities.

Main Methods:

  • Fabrication of a silicon synaptic transistor with two independent gates.
  • Measurement and analysis of STDP characteristics.
  • Device modeling based on experimental data.
  • Demonstration of pattern recognition using the Modified National Institute of Standards and Technology (MNIST) dataset.

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Main Results:

  • The silicon synaptic transistor successfully emulates synaptic plasticity.
  • A hardware-based spiking neural network system was realized without switching components.
  • Pattern recognition was achieved using the developed system.
  • The essential role of inhibitory synapses in classification was confirmed.

Conclusions:

  • The developed silicon synaptic transistor is a viable component for power-efficient neuromorphic computing.
  • Inhibitory synapses are critical for high-performance pattern classification in hardware neural networks.