Deep Photonic Reservoir Computer Based on Frequency Multiplexing with Fully Analog Connection Between Layers
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929194754113536 |
|---|---|
| author | Lupo, Alessandro Picco, Enrico Zajnulina, Marina Massar, Serge |
| author_facet | Lupo, Alessandro Picco, Enrico Zajnulina, Marina Massar, Serge |
| contents | Reservoir computers (RC) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RC), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any digital processing. We find that the deep-RC outperforms a traditional RC by up to two orders of magnitude on two benchmark tasks. This work paves the way towards using fully analog photonic neuromorphic computing for complex processing of time series, while avoiding costly analog-to-digital and digital-to-analog conversions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_08892 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Photonic Reservoir Computer Based on Frequency Multiplexing with Fully Analog Connection Between Layers Lupo, Alessandro Picco, Enrico Zajnulina, Marina Massar, Serge Emerging Technologies Applied Physics Optics Reservoir computers (RC) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RC), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any digital processing. We find that the deep-RC outperforms a traditional RC by up to two orders of magnitude on two benchmark tasks. This work paves the way towards using fully analog photonic neuromorphic computing for complex processing of time series, while avoiding costly analog-to-digital and digital-to-analog conversions. |
| title | Deep Photonic Reservoir Computer Based on Frequency Multiplexing with Fully Analog Connection Between Layers |
| topic | Emerging Technologies Applied Physics Optics |
| url | https://arxiv.org/abs/2305.08892 |