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Hardware implementation of Bayesian network based on two-dimensional memtransistors

Author

Listed:
  • Yikai Zheng

    (Penn State University)

  • Harikrishnan Ravichandran

    (Penn State University)

  • Thomas F. Schranghamer

    (Penn State University)

  • Nicholas Trainor

    (Penn State University
    Penn State University)

  • Joan M. Redwing

    (Penn State University
    Penn State University)

  • Saptarshi Das

    (Penn State University
    Penn State University
    Penn State University
    Penn State University)

Abstract

Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining ‘1’ in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33053-x
    DOI: 10.1038/s41467-022-33053-x
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    References listed on IDEAS

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    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. Thomas F. Schranghamer & Aaryan Oberoi & Saptarshi Das, 2020. "Graphene memristive synapses for high precision neuromorphic computing," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    5. 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.
    6. Pin-Chun Shen & Cong Su & Yuxuan Lin & Ang-Sheng Chou & Chao-Ching Cheng & Ji-Hoon Park & Ming-Hui Chiu & Ang-Yu Lu & Hao-Ling Tang & Mohammad Mahdi Tavakoli & Gregory Pitner & Xiang Ji & Zhengyang Ca, 2021. "Ultralow contact resistance between semimetal and monolayer semiconductors," Nature, Nature, vol. 593(7858), pages 211-217, May.
    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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    Cited by:

    1. 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.
    2. Subir Ghosh & Andrew Pannone & Dipanjan Sen & Akshay Wali & Harikrishnan Ravichandran & Saptarshi Das, 2023. "An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Muhtasim Ul Karim Sadaf & Najam U Sakib & Andrew Pannone & Harikrishnan Ravichandran & Saptarshi Das, 2023. "A bio-inspired visuotactile neuron for multisensory integration," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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