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On-device synaptic memory consolidation using Fowler-Nordheim quantum-tunneling.

Mustafizur Rahman1, Subhankar Bose1, Shantanu Chakrabartty1

  • 1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States.

Frontiers in Neuroscience
|January 30, 2023
PubMed
Summary

A new Fowler-Nordheim (FN) quantum tunneling synapse offers a simpler, more efficient approach to artificial memory consolidation. This FN-synapse demonstrates near-optimal performance and energy efficiency for continual learning tasks.

Keywords:
continual learninghardware synapsememory consolidationneuromorphicquantum-tunneling

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Area of Science:

  • * Neuromorphic Engineering
  • * Quantum Physics Applications

Background:

  • * Classical physics-based artificial synapses are complex and difficult to scale for optimal memory consolidation.
  • * Existing models struggle with bounded strengths and finite precision updates.

Purpose of the Study:

  • * To introduce a simpler artificial synapse model using Fowler-Nordheim (FN) quantum tunneling.
  • * To demonstrate tunable memory consolidation with adjustable plasticity-stability trade-offs.
  • * To evaluate the performance of FN-synapses in large-scale memory consolidation and continual learning.

Main Methods:

  • * Fabrication of a prototype FN-synapse array using standard silicon processes.
  • * Verification of optimal memory consolidation characteristics and parameter estimation for an analytical FN-synapse model.
  • * Implementation of large-scale memory consolidation and continual learning experiments using the analytical model.

Main Results:

  • * FN-synapses exhibit near-optimal synaptic lifetime and consolidation properties compared to other physical implementations.
  • * A network of FN-synapses outperformed a comparable elastic weight consolidation (EWC) network on benchmark continual learning tasks.
  • * Demonstrated ultra-energy-efficient operation with femtojoules per synaptic update.

Conclusions:

  • * The FN-synapse offers a simplified and highly efficient physical device for synaptic memory consolidation and continual learning.
  • * This approach achieves tunable memory consolidation characteristics with favorable plasticity-stability trade-offs.
  • * The ultra-low energy footprint makes FN-synapses a promising solution for energy-efficient neuromorphic computing.