An atomic Boltzmann machine capable of self-adaption
نویسندگان
چکیده
The Boltzmann Machine (BM) is a neural network composed of stochastically firing neurons that can learn complex probability distributions by adapting the synaptic interactions between neurons. BMs represent very generic class stochastic networks be used for data clustering, generative modelling and deep learning. A key drawback software-based required Monte Carlo sampling, which scales intractably with number Here, we realize physical implementation BM directly in spin dynamics gated ensemble coupled cobalt atoms on surface semiconducting black phosphorus. Implementing concept orbital memory utilizing scanning tunnelling microscopy, demonstrate bottom-up construction atomic ensembles whose current noise defined reconfigurable multi-well energy landscape. Exploiting anisotropic behaviour phosphorus, build two well-separated intrinsic time synapses. By characterizing conditional steady-state distribution given configurations, illustrate an many distinct distributions. probing dynamics, reveal autonomous reorganization synapses response to external electrical stimuli. This self-adaptive architecture paves way on-chip learning atomic-scale machine hardware.
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ژورنال
عنوان ژورنال: Nature Nanotechnology
سال: 2021
ISSN: ['1748-3395', '1748-3387']
DOI: https://doi.org/10.1038/s41565-020-00838-4