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Signal and noise extraction from analog memory elements for neuromorphic computing.

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  • 1IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, 10598, USA.

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|May 31, 2018
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Summary
This summary is machine-generated.

A new Gaussian process regression method accurately characterizes non-volatile memory (NVM) devices for neuromorphic computing. This technique separates signal from noise in NVM, enabling more efficient and parallel computing systems.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Neuromorphic computing systems require non-volatile memory (NVM) elements with analog-like conductance tuning and symmetric switching.
  • Existing NVM devices often exhibit non-linear and asymmetric switching, complicating signal and noise separation.
  • Conventional characterization techniques struggle with the complex behaviors of NVM devices.

Purpose of the Study:

  • To develop a practical methodology for characterizing NVM devices for neuromorphic computing.
  • To address the challenge of separating signal and noise in NVM devices with non-linear switching.
  • To enable the realization of ideal NVM devices for energy-efficient and parallel computing.

Main Methods:

  • Establishment of a practical methodology based on Gaussian process regression.
  • The methodology is agnostic to specific switching mechanisms and applicable to diverse NVM devices.
  • Characterization of 1000 phase-change memory devices based on Ge2Sb2Te5.

Main Results:

  • Demonstrated the tradeoff between switching symmetry and signal-to-noise ratio in HfO2-based resistive random-access memory.
  • Successfully separated total variability into device-to-device variability and inherent randomness for Ge2Sb2Te5 phase-change memory devices.
  • Validated the methodology's effectiveness in analyzing NVM device characteristics.

Conclusions:

  • The developed Gaussian process regression methodology provides a practical solution for characterizing NVM devices.
  • This approach is crucial for understanding and mitigating variability in NVM for neuromorphic applications.
  • The methodology facilitates the development of superior NVM devices essential for advancing neuromorphic computing.