A memristor-based Bayesian machine

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Neuro-inspired computing chips

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Massively parallel Bayesian inference for transient gravitational-wave astronomy

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Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

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In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling

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A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices

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Dark Memory and Accelerator-Rich System Optimization in the Dark Silicon Era

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HERMES-Core—A 1.59-TOPS/mm ² PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs

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33.1 A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models

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Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

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Binary multiplication with PN sequences

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Implementing Bayesian networks with embedded stochastic MRAM

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VIBNN

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Bayesian Inference With Muller C-Elements

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Real-time room occupancy estimation with Bayesian machine learning using a single PIR sensor and microcontroller

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Heavy Ion Radiation Effects on Hafnium Oxide-Based Resistive Random Access Memory

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Bayesian real-time perception algorithms on GPU

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CMOS-integrated nanoscale memristive crossbars for CNN and optimization acceleration

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FPGA implementation of Bayesian network inference for an embedded diagnosis

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Bayesian Inference Based Robust Computing on Memristor Crossbar

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An Unbiased MCMC FPGA-Based Accelerator in the Land of Custom Precision Arithmetic

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eNVM RRAM reliability performance and modeling in 22FFL FinFET technology

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Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

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The Probabilistic Cell: Implementation of a Probabilistic Inference by the Biochemical Mechanisms of Phototransduction

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A 3mm 2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm

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A Bayesian Stochastic Machine for Sound Source Localization

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Neuromorphic computing at scale

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Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing

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Roadmap to neuromorphic computing with emerging technologies

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A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

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Memristor-Based Artificial Chips

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Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

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Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

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Quantum-limited stochastic optical neural networks operating at a few quanta per activation

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Spatiotemporal audio feature extraction with dynamic memristor-based time-surface neurons

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Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications

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A dual-domain compute-in-memory system for general neural network inference

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SpinDrop: Dropout-Based Bayesian Binary Neural Networks With Spintronic Implementation

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Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator

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Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis

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Transforming memristor noises into computational innovations

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Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses

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The inherent adversarial robustness of analog in-memory computing

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Leveraging stochasticity in memristive synapses for efficient and reliable neuromorphic systems

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Implementation of Bayesian networks and Bayesian inference using a Cu 0.1 Te 0.9 /HfO 2 /Pt threshold switching memristor

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Random resistive memory-based deep extreme point learning machine for unified visual processing

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High-Performance Edge-Line Contact Memristors with In-Plane Solid–Liquid–Solid Grown Silicon Nanowires for Probabilistic Neuromorphic Computing

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Resistive switching in graphene: A theoretical case study on the alumina-graphene interface

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Heisenberg machines with programmable spin circuits

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Memristor-based Neuromorphic Diagnostic Systems: An Emerging Strategy for Intelligent Medicine

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Superior probabilistic computing using operationally stable probabilistic-bit constructed by a manganite nanowire

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Binary Bayesian Neural Networks for Efficient Uncertainty Estimation Leveraging Inherent Stochasticity of Spintronic Devices

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Solving inference problems of Bayesian networks by probabilistic computing

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FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing

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High-Performance In-Memory Bayesian Inference With Multi-Bit Ferroelectric FET

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Recent progress on integrated neuromorphic chips

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KERNTROL: Kernel Shape Control Toward Ultimate Memory Utilization for In-Memory Convolutional Weight Mapping

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Stochastic logic in biased coupled photonic probabilistic bits

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The logarithmic memristor-based Bayesian machine

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Exploiting Read Current Noise of TiOx Resistive Memory by Controlling Forming Conditions for Probabilistic Neural Network Hardware

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Bayesian In-Memory Computing with Resistive Memories

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NMBNN: Noise-Adaptive Memristive Bayesian Neural Network for Energy-Efficient Edge Health Care

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Energy-Efficient Bayesian Inference Using Near-Memory Computation with Memristors

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Probabilistic Bitstream Generator based on Superparamagnetic Tunnel Junctions

Danouchi, K., Prenat, G., & Anghel, L. (2023) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano58406.2023.10231253 · Cited by 2 · see citing works

MeMCISA: Memristor-Enabled Memory-Centric Instruction-Set Architecture for Database Workloads

Zhu, Y., Cai, L., Yu, L., Fan, A., Yan, L., Jing, Z., Yan, B., Tiw, P. J., Li, Y., Tao, Y., et al. (2024) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro61859.2024.00122 · Cited by 1 · see citing works

A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model

Dai, Y., Tan, S. E., Feng, Z., Zou, J., Guo, W., Yu, R., Wang, Z., Hu, Y., Wang, H., Ruan, H., et al. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad9a1c · Cited by 1 · see citing works

Exploiting Device Nonuniformity: β-Ga₂O₃ Arrays for Integrated Sensing-Computing Bayesian Neural Networks

Liu, Y., Wang, Y., Wang, X., Yang, C., Li, L., Tang, W., & Guo, Y. (2025) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2025.3599823 · Cited by 1 · see citing works

A tiny ‘Bayesian machine’ does much with little

(2022) · Nature · doi:10.1038/d41586-022-04495-6

Bayesian Nanoelectronics

Querlioz, D. (2023) · Silicon Nanoelectronics Workshop (SNW) · doi:10.23919/snw57900.2023.10183919

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Frick, N. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1003254

Stochastic computing with biased optical parametric oscillators

Roques‐Carmes, C., Salamin, Y., Choi, S., Horodynski, M., Sloan, J., Luo, D., & Marin Soljacic, A. (2025) · AI and Optical Data Sciences · doi:10.1117/12.3037014

Bayesian-Informed Hyperdimensional Learning for Intelligent and Efficient Data Processing

Barkam, H. E., Das, T., Poduval, P., Jeong, S., Yeung, C., Solitan, M. A., & Imani, M. (2024) · doi:10.1145/3676536.3676684

Experimental Demonstration of Stochastic Bayesian Inference Using Müller C-Elements

Edwards, A. J., Usih, E. C., Zhou, P., Hill, B. A., Martindell, S., Qi, T., Biswas, D., Hu, X., Panduranga, S. M., & Friedman, J. S. (2025) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2025.3591067

光学神经网络：硬件物理与软件算法的融合进展（特邀）

Hao, 王. W., Ziyu, 战. Z., Xing, 付. F., & Qiang, 柳. L. (2025) · Acta Optica Sinica · doi:10.3788/aos250861

Deterministic 1D Domain Wall Motion with Nucleation-Free Nature in Sliding Ferroelectric Switching

Chen, J., Ke, C., Bian, R., Pan, E., Kun, P., Li, Z., Dong, B., Yang, F., Liu, Q., Tapasztó, L., et al. (2026) · Physical Review X · doi:10.1103/q624-x397

Energy-Efficient Memristor-Based Artificial Intelligence Accelerators using In/Near Memory Computing

Harabi, K.-E. (2023) · doi:10.70675/0d4bc45bzeef3z40b2z8ce9z8b08b84ee143 · Cited by 1 · see citing works

Embracing Imperfections : Hardware-compatible Neural Networks for Neuromorphic Computing

Majumdar, A. (2023) · doi:10.70675/4bf81ddbz25cfz48c6zaf34zcaedca216e17

Physics-Grounded Neuromorphic Computing : From Spiking Neurons to Learning Algorithms

Drouhin, M. (2023) · theses.fr (ABES) · doi:10.70675/f8687efbz8f22z4022zb65czb59d11e98482

Synaptic uncertainty for in-memory computing

Bonnet, D. (2024) · doi:10.70675/5d34cb96z0f54z44b0zb3aez48f1fbf1e560

Design and reliability study of crossbar circuits based on multilevel spintronic devices for Artificial Intelligence.

Danouchi, K. (2024) · doi:10.70675/d792b841z8f1ez4d85zbb04zc2d41a261e31

A Low-Power Bayesian Head Using SOT-MRAM Arrays for Uncertainty-Aware Binary Neural Networks

Palhares, J. H. Q., Lovison-Franco, B., Hutin, L., Miquel, J., Harabi, K.-E., Romdhane, A., & Garello, K. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539540

RRAM-as-Reference Sensing with Parallelogram Crossbar Architecture for Large-Scale Arrays

Guo, R., Pechmann, S., Baroni, A., Pérez, E., Wenger, C., Xu, P., & Hagelauer, A. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562460

In-Memory Bayesian Machine Using Vertical Cu0.33Te0.67/HfO2/TiN Memristive Crossbar Arrays

Baek, I. K., Cheong, S., Kim, H. W., Cho, J. M., Son, K., Kim, Y. R., Woo, K. S., Park, H., Kim, B. S., & Hwang, C. S. (2026) · ACS Nano · doi:10.1021/acsnano.6c07852

Thermo-Physical Modeling and Intelligent Learning-Based Topographical Characterization of Micro-Dimples During Laser Pulsed Processing

Singh, B. K., Kishore, H., Kapil, S., & Joshi, S. N. (2026) · Lasers in Manufacturing and Materials Processing · doi:10.1007/s40516-026-00378-0

Energy-Efficient Bayesian Inference Using Near-Memory Computation with Memristors

Turck, C., Harabi, K.-E., Hirtzlin, T., Vianello, E., Laurent, R., Jacques Droulez, A., Bessìère, P., Bocquet, M., Portal, J. M., & Querlioz, D. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137312 · Cited by 3 · see citing works

Memristors for Bayesian in-memory computing

Dalgaty, T., Vianello, E., & Querlioz, D. (2025) · Nature Materials · doi:10.1038/s41563-025-02409-1 · Cited by 3 · see citing works

Harnessing intrinsic memristor randomness with Bayesian neural networks

Dalgaty, T., Vianello, E., & Querlioz, D. (2021) · IEEE International Conference on IC Design and Technology (ICICDT) · doi:10.1109/icicdt51558.2021.9626535 · Cited by 5 · see citing works

Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

Bonnet, D., Hirtzlin, T., Majumdar, A., Dalgaty, T., Esmanhotto, E., Meli, V., Castellani, N., Martin, S., Nodin, J.-F., Bourgeois, G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43317-9 · Cited by 75 · see citing works

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Frick, N. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1003254