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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Related Experiment Video

Updated: Jul 10, 2026

The TD Drive: A Parametric, Open-Source Implant for Multi&#45;Area Electrophysiological Recordings in Behaving and Sleeping Rats
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Multibit neural inference in a N-ary crossbar architecture.

Anatole Moureaux1, Anthony Lopes Temporao2, Flavio Abreu Araujo2

  • 1Institute of Condensed Matter and Nanosciences, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium. anatole.moureaux@uclouvain.be.

Scientific Reports
|July 8, 2026
PubMed
Summary

This study simulates in-memory computing (IMC) using N-ary crossbar arrays for efficient neural network inference. Researchers achieved 93.56% accuracy on MNIST, identifying weight quantization as a key error source.

Keywords:
N-aryCrossbarIn-memory computingMemristorsMultiply-and-accumulate

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Area of Science:

  • Hardware acceleration for artificial intelligence
  • Non-volatile memory technologies
  • Neuromorphic computing architectures

Background:

  • Conventional von Neumann architectures face energy efficiency challenges in AI tasks.
  • In-memory computing (IMC) offers a paradigm shift by performing computations directly within memory arrays.
  • Crossbar arrays, particularly those using magnetic tunnel junctions (MTJ), are promising for IMC.

Purpose of the Study:

  • To present a simulation framework for N-ary crossbar architectures in IMC.
  • To evaluate the performance of IMC for neural network inference tasks like XOR and MNIST classification.
  • To analyze error sources and optimize N-ary crossbar designs for improved accuracy and efficiency.

Main Methods:

  • Developed a simulation framework for N-ary crossbar architectures with minimal implementation assumptions.
  • Utilized a simulated (4x4) 4-state magnetic tunnel junction (MTJ) crossbar array.
  • Performed XOR and MNIST classification tasks, including PCA dimensionality reduction for performance analysis.
  • Investigated the impact of weight quantization, systematic non-idealities, and random noise on MVM accuracy.

Main Results:

  • Successfully inferred XOR and MNIST classification tasks using the simulated MTJ crossbar array.
  • Achieved 93.56% accuracy on MNIST classification, compared to a 97.56% software baseline.
  • Demonstrated that PCA dimensionality reduction significantly reduces operations with only a modest accuracy decrease.
  • Identified weight quantization as the primary error source, finding cell-specific random noise less detrimental than systematic errors.

Conclusions:

  • The simulation framework provides a viable approach for evaluating N-ary crossbar IMC architectures.
  • Weight quantization is a critical factor limiting accuracy in MTJ-based IMC, necessitating careful management.
  • Optimal cell state configuration can balance quantization errors and resistance resolution to minimize MVM errors, paving the way for more efficient AI hardware.