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A fully hardware-based memristive multilayer neural network.

Fatemeh Kiani1, Jun Yin1, Zhongrui Wang1

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This study demonstrates a fully analog neural network using memristive crossbar arrays. By implementing activation functions in hardware, it achieves high accuracy for handwritten digit recognition with improved efficiency.

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Area of Science:

  • Neuromorphic Engineering
  • In-memory Computing
  • Artificial Intelligence Hardware

Background:

  • Memristive crossbar arrays offer potential for efficient in-memory analog computing.
  • Prior machine learning with memristive arrays often required digital components, limiting energy efficiency and parallelism.

Purpose of the Study:

  • To develop a fully analog neural network by implementing activation functions in hardware.
  • To eliminate analog/digital conversions for enhanced computing throughput and power efficiency.

Main Methods:

  • Designed and constructed compact rectified linear units (ReLUs) for analog activation functions.
  • Built a two-layer perceptron utilizing memristive crossbar arrays for computation.

Main Results:

  • Achieved 93.63% recognition accuracy on the MNIST handwritten digits dataset.
  • Demonstrated a fully hardware-based neural network, reducing data conversion and shuttling.

Conclusions:

  • Implementing activation functions in analog hardware enables fully analog signal transmission between neural network layers.
  • The proposed approach significantly enhances computing throughput and power efficiency compared to hybrid systems.