A 24-node array of diffractively coupled VCSEL lasers serves as an optical reservoir computer, reaching below 1% bit error on 2-bit XOR and 3-bit header recognition, and RMSE 0.067 on 2-bit DAC.
Comment on "All-optical machine learning using diffractive deep neural networks"
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well justified and represent a mischaracterization of the system by overlooking its defining characteristics of perfect linearity and strict passivity.
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Experimental reservoir computing with diffractively coupled VCSELs
A 24-node array of diffractively coupled VCSEL lasers serves as an optical reservoir computer, reaching below 1% bit error on 2-bit XOR and 3-bit header recognition, and RMSE 0.067 on 2-bit DAC.