Towards integrated parallel photonic reservoir computing based on frequency multiplexing

Wosen Kassa, Evangelia Dimitriadou, Marc Haelterman, Serge Massar, Erwin Bente

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)


Photonic reservoir computing uses recent advances in machine learning, and in particular the reservoir computing algorithm, to carry out complex computations optically. Experimental demonstrations with performance comparable to state of the art digital implementations have been reported. However, most experiments so far were based on sequential processing using time-multiplexing. Parallel architectures promise considerable speedup. Recently, a reservoir computing architecture based on frequency parallelism was proposed by our laboratory, and a preliminary demonstration was carried out using optical fibres. In this system the reservoir is linear and the nonlinearity is provided by readout photodiodes. Here, we study in simulation an implementation of this frequency parallel architecture on an InP chip using a generic integration platform. This would dramatically reduce the footprint and cost of the reservoir. The input signal is encoded by modulating the frequency comb produced by a mode locked laser with a repetition rate of 10GHz. The update rate of the input is 2.5GHz. The reservoir, an active cavity with a time delay of 0.4ns, contains a phase modulator which is driven by a 10GHz RF signal, and a semiconductor amplifier to compensate the losses in the cavity. Readout is carried out by measuring the intensity of individual frequency combs and linearly combining them. We performed time domain simulations on a standard channel equalization task. The simulation takes in to account the phase and amplitude noise of the laser source, and the amplifier noise. The power leakage between neighboring channels at the de-multiplexer is also included. To evaluate the system performance, noise is added as a global parameter on the input signal to assess the SNR requirements. Simulation results show that the laser phase noise is far more important that other types of noise, hence the laser source design/operation should be optimized to achieve low phase noise comb.

Original languageEnglish
Title of host publicationNeuro-inspired Photonic Computing
EditorsPeter Bienstman, Marc Sciamanna
Number of pages7
ISBN (Print)9781510619043
Publication statusPublished - 1 Jan 2018
EventSPIE Photonics Europe - Strasbourg, France
Duration: 22 Apr 201826 Apr 2018

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X


ConferenceSPIE Photonics Europe
OtherNeuro-inspired Photonic Computing 2018
Internet address


  • photonic reservoir computing
  • parallel computing
  • artificial neural networks
  • channel equalization


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