Trainable dynamical masking for readout-free optical computing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912402401918976 |
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| author | Bogdanov, S. Manuylovich, E. Turitsyn, S. K. |
| author_facet | Bogdanov, S. Manuylovich, E. Turitsyn, S. K. |
| contents | Nonlinear systems, transforming an input signal into a high-dimensional output feature space, can be used for non-conventional computing. This approach, however, requires a change of system parameters during training rather than coefficients in a software program. We propose here to use available off-the-shelf high-speed optical communication devices and technologies to implement a trainable dynamical mask in addition to or even instead of the traditional readout layer for extreme learning machine-based computing. The computational potential of the proposed approach is demonstrated with both regression and time series prediction tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23464 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trainable dynamical masking for readout-free optical computing Bogdanov, S. Manuylovich, E. Turitsyn, S. K. Optics Nonlinear systems, transforming an input signal into a high-dimensional output feature space, can be used for non-conventional computing. This approach, however, requires a change of system parameters during training rather than coefficients in a software program. We propose here to use available off-the-shelf high-speed optical communication devices and technologies to implement a trainable dynamical mask in addition to or even instead of the traditional readout layer for extreme learning machine-based computing. The computational potential of the proposed approach is demonstrated with both regression and time series prediction tasks. |
| title | Trainable dynamical masking for readout-free optical computing |
| topic | Optics |
| url | https://arxiv.org/abs/2505.23464 |