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Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design.

Do Hoon Kim1, Woon Hyung Cheong2, Hanchan Song1

  • 1Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. km.kim@kaist.ac.kr.

Materials Horizons
|June 25, 2024
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Summary
This summary is machine-generated.

We developed energy-efficient hardware using memristive Monte Carlo DropConnect (MC-DC) for neuromorphic computing. This co-design approach optimized algorithms for hardware, improving neural network performance.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • Neuromorphic computing algorithms are increasing in complexity, demanding specialized hardware for efficient implementation.
  • Device and algorithm co-design offers a pathway to create energy-efficient hardware tailored for complex computational tasks.
  • Existing hardware solutions face challenges in meeting the demands of advanced neuromorphic algorithms.

Purpose of the Study:

  • To present a memristive Monte Carlo DropConnect (MC-DC) crossbar array developed via a hardware-algorithm co-design strategy.
  • To demonstrate the integration of stochastic switching and analog memory characteristics using specific memristor devices.
  • To showcase the successful implementation and performance enhancement of the MC-DC neural network on the co-designed hardware.

Main Methods:

  • Developed a memristive crossbar array using Ag-based diffusive selectors and Ru-based electrochemical metalization (ECM) memristors.
  • Integrated devices into a one-selector one-memristor (1S1M) structure, achieving well-matched operating voltages and currents.
  • Implemented the Monte Carlo DropConnect (MC-DC) algorithm on the fabricated hardware, enabling stochastic readout and analog programming.

Main Results:

  • Successfully demonstrated the MC-DC operation on the integrated memristive crossbar array.
  • Achieved both stochastic switching (via selectors) and analog memory (via memristors) functionalities.
  • Modified the MC-DC algorithm based on selector-controlled switching polarity, leading to improved network performance.

Conclusions:

  • The hardware-algorithm co-design approach is effective for developing energy-efficient neuromorphic computing hardware.
  • Memristive devices, particularly Ag/Ru-based 1S1M structures, are suitable for implementing complex algorithms like MC-DC.
  • Understanding and adapting algorithms to specific hardware characteristics, like switching polarity, can significantly enhance neural network performance.