An atomic Boltzmann machine capable of self-adaption
| Autor: | Hilbert J. Kappen, Elze J. Knol, Werner M. J. van Weerdenburg, Brian Kiraly, Alexander A. Khajetoorians |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Scanning Probe Microscopy Biomedical Engineering Boltzmann machine Biophysics Energy landscape Bioengineering 02 engineering and technology Orbital mechanics 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Atomic and Molecular Physics and Optics 0104 chemical sciences Computational science Limit (music) Piecewise Probability distribution General Materials Science Electrical and Electronic Engineering 0210 nano-technology Scaling Realization (systems) |
| Zdroj: | Nature Nanotechnology, 16, 414-420 Nature Nanotechnology Nature Nanotechnology, 16, pp. 414-420 |
| ISSN: | 1748-3387 |
| Popis: | The quest to implement machine learning algorithms in hardware has focused on combining various materials, each mimicking a computational primitive, to create device functionality. Ultimately, these piecewise approaches limit functionality and efficiency, while complicating scaling and on-chip learning, necessitating new approaches linking physical phenomena to machine learning models. Here, we create an atomic spin system that emulates a Boltzmann machine directly in the orbital dynamics of one well-defined material system. Utilizing the concept of orbital memory based on individual cobalt atoms on black phosphorus, we fabricate the prerequisite tuneable multi-well energy landscape by gating patterned atomic ensembles using scanning tunnelling microscopy. Exploiting the anisotropic behaviour of black phosphorus, we realize plasticity with multi-valued and interlinking synapses that lead to tuneable probability distributions. Furthermore, we observe an autonomous reorganization of the synaptic weights in response to external electrical stimuli, which evolves at a different time scale compared to neural dynamics. This self-adaptive architecture paves the way for autonomous learning directly in atomic-scale machine learning hardware. Stochastic orbital dynamics of individually coupled Co atoms on black phosphorus enables the realization of a Boltzmann machine capable of self-adaption. |
| Databáze: | OpenAIRE |
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