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Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning.

Seokjin Oh1, Jiyong An1, Seungmyeong Cho1

  • 1School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.

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|July 29, 2023
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Summary
This summary is machine-generated.

This study introduces a novel analog circuit for Equilibrium Propagation (EP), enabling practical on-device learning in edge intelligence hardware. The new design simplifies implementation by calculating solutions simultaneously and using a modified learning rule.

Keywords:
equilibrium propagationlocal learningmemristor crossbar circuitson-device learning

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence Hardware
  • Machine Learning Algorithms

Background:

  • Equilibrium Propagation (EP) is a local learning algorithm for neural networks.
  • Numerical EP implementation is challenging for edge intelligence hardware due to iterative calculations.
  • Existing analog circuit solutions for EP face challenges like memory requirements and peripheral circuit complexity.

Purpose of the Study:

  • To propose a new analog circuit technique for practical and implementable realization of the EP algorithm.
  • To address limitations of previous analog circuit implementations for EP.
  • To enable on-device learning in edge intelligence hardware using EP.

Main Methods:

  • Developed analog circuits to simultaneously calculate free-phase and nudge-phase solutions, eliminating the need for free-phase solution memory.
  • Proposed and implemented a simplified EP learning rule based on fixed conductance change per programming pulse.
  • Simulated and verified a memristor conductance update circuit for synaptic weight training on memristor crossbars.

Main Results:

  • The proposed analog circuit technique successfully eliminates the need for storing free-phase solutions.
  • The modified EP learning rule enables practical and implementable weight update circuits without complex verification.
  • Simulations confirmed the efficacy of the memristor conductance update circuit for training synaptic weights.

Conclusions:

  • The novel analog circuit technique provides a practical and implementable solution for realizing the Equilibrium Propagation algorithm in hardware.
  • This advancement facilitates on-device learning capabilities crucial for edge intelligence applications.
  • The simplified learning rule and simultaneous solution calculation overcome key implementation hurdles.