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Non-ideal Resistive Random Access Memory (RRAM) devices show promise for power-efficient artificial intelligence (AI) hardware. Even with device imperfections, accurate machine learning (ML) inference is achievable, especially with a sufficient resistance state ratio.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Resistive Random Access Memory (RRAM) offers potential for low-power hardware in AI and ML.
  • Non-von Neumann architectures are being explored for enhanced computational efficiency.
  • The impact of RRAM device non-idealities on AI/ML performance remains a critical research question.

Purpose of the Study:

  • To investigate whether device non-idealities in RRAM preclude its use in AI/ML inference.
  • To evaluate the performance of non-ideal silicon oxide (SiO2) RRAM devices for handwritten digit classification.
  • To identify key device properties influencing classification accuracy.

Main Methods:

  • Utilized experimental data from SiO2 RRAM devices as proxies for physical weights.
  • Performed classification tasks on the MNIST dataset using these non-ideal RRAM devices.
  • Analyzed the impact of various non-idealities including resistance state ratio, discrete states, yield, stuck states, and non-linearities.

Main Results:

  • Acceptable classification accuracies were achieved using non-ideal RRAM devices.
  • A high-to-low resistance state ratio greater than 3 was crucial for accuracy, achieving ~96.8%.
  • Compared to ideal weights (~97.3% accuracy), non-ideal devices demonstrated comparable performance, with specific non-idealities impacting results.

Conclusions:

  • Non-ideal RRAM devices are viable for AI/ML inference tasks.
  • The resistance state ratio is a critical factor for achieving high accuracy.
  • Understanding and optimizing device non-idealities is essential for robust RRAM-based AI hardware.