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Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks

Author

Listed:
  • Amritanand Sebastian

    (Penn State University)

  • Rahul Pendurthi

    (Penn State University)

  • Azimkhan Kozhakhmetov

    (Penn State University)

  • Nicholas Trainor

    (Penn State University
    Penn State University)

  • Joshua A. Robinson

    (Penn State University
    Penn State University
    Penn State University)

  • Joan M. Redwing

    (Penn State University
    Penn State University
    Penn State University)

  • Saptarshi Das

    (Penn State University
    Penn State University
    Penn State University)

Abstract

Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.

Suggested Citation

  • Amritanand Sebastian & Rahul Pendurthi & Azimkhan Kozhakhmetov & Nicholas Trainor & Joshua A. Robinson & Joan M. Redwing & Saptarshi Das, 2022. "Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33699-7
    DOI: 10.1038/s41467-022-33699-7
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    References listed on IDEAS

    as
    1. Akhil Dodda & Aaryan Oberoi & Amritanand Sebastian & Tanushree H. Choudhury & Joan M. Redwing & Saptarshi Das, 2020. "Stochastic resonance in MoS2 photodetector," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    2. Yikai Zheng & Harikrishnan Ravichandran & Thomas F. Schranghamer & Nicholas Trainor & Joan M. Redwing & Saptarshi Das, 2022. "Hardware implementation of Bayesian network based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Amritanand Sebastian & Andrew Pannone & Shiva Subbulakshmi Radhakrishnan & Saptarshi Das, 2019. "Gaussian synapses for probabilistic neural networks," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    4. Shiva Subbulakshmi Radhakrishnan & Amritanand Sebastian & Aaryan Oberoi & Sarbashis Das & Saptarshi Das, 2021. "A biomimetic neural encoder for spiking neural network," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    5. Kwon, Yongchan & Won, Joong-Ho & Kim, Beom Joon & Paik, Myunghee Cho, 2020. "Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    6. Lukas Mennel & Joanna Symonowicz & Stefan Wachter & Dmitry K. Polyushkin & Aday J. Molina-Mendoza & Thomas Mueller, 2020. "Ultrafast machine vision with 2D material neural network image sensors," Nature, Nature, vol. 579(7797), pages 62-66, March.
    7. Sarbashis Das & Akhil Dodda & Saptarshi Das, 2019. "A biomimetic 2D transistor for audiomorphic computing," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
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