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Artificial Intelligence Goes Physical
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DOI：10.1002/smsc.202000065.png)
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Abstract
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Exploiting the intrinsic nonlinearity in physical reservoirs, e.g., dopant-atom networks, provides a new approach toward highly efficient computing such as feature projection and classification. In a recent study by Chen et al., the computational capability of dopant-atom network was investigated and found to diminish as the signal-to-noise ratio (SNR) increased, indicating the existence of an optimal bias condition. Although high SNR is often pursued in signal processing, it shows that embracing noise in non-conventional computing systems may lead to a leap in computing capacity. This work showcased that material or device physics in different domains offer valuable substrates for complex computing functions and high energy efficiency.
Keywords:
artificial intelligence
nonlinearity
noise
reservoir computing
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NATURE ELECTRONICS
NATURE ELECTRONICSIF40.9
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