# Scalable wavelength-multiplexing photonic reservoir computing

arXiv: https://arxiv.org/abs/2305.14927 · HTML: https://mlsanity.com/paper/2305.14927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 2 of 2)

- [Multi-Task Wavelength-Multiplexed Reservoir Computing Using a Silicon Microring Resonator](https://mlsanity.com/paper/2310.16588/) (2023)
- [Multi-task Photonic Reservoir Computing: Wavelength Division Multiplexing for Parallel Computing with a Silicon Microring Resonator](https://mlsanity.com/paper/2407.21189/) (2024)

## Abstract (arXiv)

Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes construct connected physical neurons in parallel, while an optical feedback loop provides interactive virtual neurons in series. We experimentally demonstrate a four-channel wavelength-multiplexing PRC, which runs four times faster than the single-channel case. It is proved that the multiplexing PRC exhibits superior performance on the task of signal equalization in an optical fiber communication link. Particularly, this scheme is highly scalable owing to the rich mode resources in Fabry-Perot lasers.