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Fully hardware-implemented memristor convolutional neural network.

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|January 31, 2020
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High-performance memristor crossbar arrays enable efficient hardware implementation of convolutional neural networks (CNNs). This neuromorphic system achieves over 96% accuracy on image recognition tasks with superior energy efficiency compared to GPUs.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Science

Background:

  • Memristor-based neuromorphic computing offers fast, energy-efficient neural network training.
  • Hardware implementation of convolutional neural networks (CNNs) using memristor crossbars remains challenging due to device imperfections.
  • Achieving software-comparable results in memristor-based CNNs is difficult due to device variability and low yield.

Purpose of the Study:

  • To fabricate high-yield, uniform memristor crossbar arrays for CNN implementation.
  • To develop a hybrid-training method to overcome device non-idealities.
  • To demonstrate a scalable memristor-based CNN for image recognition and edge computing.

Main Methods:

  • Fabrication of integrated memristor crossbar arrays (eight 2,048-cell arrays).
  • Development and application of a hybrid-training approach to adapt to device variations.
  • Implementation of a five-layer memristor-based CNN for MNIST image recognition.

Main Results:

  • Achieved high accuracy (>96%) in MNIST image recognition using the memristor-based CNN.
  • Demonstrated parallel processing capabilities, including parallel convolutions and processing different inputs concurrently.
  • Exhibited energy efficiency over two orders of magnitude greater than state-of-the-art GPUs.
  • Showcased scalability to larger neural network architectures like residual neural networks.

Conclusions:

  • Viable memristor-based non-von Neumann hardware solutions are enabled for deep neural networks.
  • The developed system offers a promising path for energy-efficient edge computing applications.
  • High-performance and uniform memristor arrays, coupled with adaptive training, overcome previous limitations in hardware CNN implementation.