ArticleNature communications2023
Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 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
- Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Hardware Acceleration of Stochastic Neural Networks Enabled by Bit-Cell Level Co-Design of Magnetic Tunnel Junctions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Memristors for Bayesian in-memory computing.Nature materials · 2026Article
- Coupled dual-channel memristors for hardware-native trustworthy Bayesian intelligence.Nature communications · 2026Article
- Homeostatic dendritic neuron based on co-integrated volatile and non-volatile memristors for neuromorphic processing.Nature communications · 2026Article
- Intrinsic annealing in a hybrid memristor-magnetic tunnel junction Ising machine.Nature communications · 2026Article
- AI-driven multiscale virtual plant cell modeling: from molecular mechanisms to tissue functions.Planta · 2026Review
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Bayesian continual learning and forgetting in neural networks.Nature communications · 2025Article
- Training of physical neural networks.Nature · 2025Review
- Ferroelectric NAND for efficient hardware bayesian 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
- Layer ensemble averaging for fault tolerance in memristive neural networks.Nature communications · 2025Article
- Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.Journal of applied clinical medical physics · 2025Article
- Article
- Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing.Research (Washington, D.C.) · 2025Review
- Probabilistic photonic computing with chaotic light.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.
Identifiers
What OpenQuestion holds
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.