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A Method for Growing Bio-memristors from Slime Mold
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Memristive Architectures Exploiting Self-Compliance Multilevel Implementation on 1 kb Crossbar Arrays for Online and

Sungjoon Kim1, Hyeonseung Ji2, Kyungchul Park3

  • 1Department of AI Semiconductor Engineering, Korea University, Sejong 30019, Republic of Korea.

ACS Nano
|August 21, 2024
PubMed
Summary

Self-compliance (SC) in memristor crossbar arrays enables transistor-less operation and vector-matrix multiplication for neuromorphic systems. This technology achieves high accuracy in MNIST classification tasks, demonstrating its potential for advanced AI hardware.

Keywords:
crossbar arraymemristorneuromorphic systemonline/offline learningself-compliance

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Memristor crossbar arrays offer a promising hardware platform for neuromorphic computing.
  • Challenges include controlling conductive filament (CF) formation and enabling efficient computation.

Purpose of the Study:

  • To investigate the practical implications of self-compliance (SC) in high-density crossbar arrays.
  • To demonstrate the feasibility of transistor-less operation and vector-matrix multiplication (VMM) using SC memristors.
  • To evaluate the performance of SC memristor crossbar arrays in neural network applications.

Main Methods:

  • Utilized an AlO/TiO internal overshoot limitation structure to achieve SC in resistive random-access memory (RRAM).
  • Optimized the AlO/TiO structure to reduce overshoot and operation current, ensuring uniform bipolar resistive switching.
  • Evaluated synaptic plasticity (LTP/LTD) through extensive electric pulse stimuli.
  • Implemented and tested a 32 × 32 crossbar array for spiking neural network (SNN)-based VMM on MNIST classification.

Main Results:

  • SC enabled transistor-less operation of the crossbar array.
  • The optimized AlO/TiO structure exhibited uniform bipolar resistive switching and analog properties.
  • Online learning neural networks achieved 92.36% MNIST accuracy using LTP/LTD characteristics.
  • Offline learning neural networks in SC mode reached 95.87% accuracy.
  • The 32 × 32 array demonstrated SNN-based VMM for MNIST classification with only a 1.2% accuracy drop compared to software.

Conclusions:

  • Self-compliance is a viable strategy for transistor-less memristor crossbar arrays.
  • The developed AlO/TiO memristor technology supports efficient VMM for neuromorphic systems.
  • The demonstrated MNIST classification accuracy highlights the potential of this hardware for AI acceleration.