ArticleNature communications2022
Hardware implementation of Bayesian network based on two-dimensional memtransistors.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Morphology-Defined Entropy Streams for Random-Bit Generation and Probabilistic Sampling in Memristor Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integration of Reconfigurable p-Bit and 1R Crossbar Array for Memristive Probabilistic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bayesian continual learning and forgetting in neural networks.Nature communications · 2025Article
- Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The logarithmic memristor-based Bayesian machine.Communications engineering · 2025Article
- 2D MoSNature communications · 2025Article
- Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing.Research (Washington, D.C.) · 2025Review
- A stochastic encoder using point defects in two-dimensional materials.Nature communications · 2024Article
- Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials.Nano-micro letters · 2024Review
- The Roadmap of 2D Materials and Devices Toward Chips.Nano-micro letters · 2024Review
- Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.Nature communications · 2023Article
- Generative complex networks within a dynamic memristor with intrinsic variability.Nature communications · 2023Article
- An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior.Nature communications · 2023Article
- A bio-inspired visuotactile neuron for multisensory integration.Nature communications · 2023Article
- Review
- Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks.Nature communications · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.