ArticleNature communications2024
CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning.
Article in Nature communications, 2024. 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.
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Who cites it
21 citing papers in PubMed.
- Hardware-Native Gaussian Sampling With Probabilistic Ising Machines for Modern Communication Paradigms.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Applied spintronics: From spin transport to spin-charge interconversion and emerging devices.Science advances · 2026Review
- Integration of Reconfigurable p-Bit and 1R Crossbar Array for Memristive Probabilistic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Probabilistic computing utilizing HfONature communications · 2026Article
- 250 magnetic tunnel junctions-based probabilistic Ising machine.Nature communications · 2026Article
- Intrinsic annealing in a hybrid memristor-magnetic tunnel junction Ising machine.Nature communications · 2026Article
- Multi-Physical Field Modulated P-Bit Device Based on VOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A parallel magnetic tunnel junction-based probabilistic Ising processor for efficient quadratic optimization.Nature communications · 2026Article
- Multi-State Probabilistic Computing Using Floating-Body MOSFETs Based on the Potts Model for Solving Complex Combinatorial Optimization Problems.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- A hardware demonstration of a universal programmable RRAM-based probabilistic computer for molecular docking.Nature communications · 2025Article
- Probabilistic greedy algorithm solver using magnetic tunneling junctions for traveling salesman problem.Nature communications · 2025Article
- Pushing the boundary of quantum advantage in hard combinatorial optimization with probabilistic computers.Nature communications · 2025Article
- Next-generation graph computing with electric current-based and quantum-inspired approaches.Nature communications · 2025Review
- ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers.Nature communications · 2025Article
- Superior probabilistic computing using operationally stable probabilistic-bit constructed by a manganite nanowire.National science review · 2025Article
- Is stochastic thermodynamics the key to understanding the energy costs of computation?Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing.Scientific reports · 2024Article
- True random number generation using the spin crossover in LaCoONature communications · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.
Identifiers
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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.