Towards integrated parallel photonic reservoir computing based on frequency multiplexing

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

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.

Originele taal-2Engels
TitelNeuro-inspired Photonic Computing
RedacteurenPeter Bienstman, Marc Sciamanna
UitgeverijSPIE
Aantal pagina's7
Volume10689
ISBN van geprinte versie9781510619043
DOI's
StatusGepubliceerd - 1 jan 2018
EvenementSPIE Photonics Europe: Neuro-inspired Photonic Computing 2018 - Strasbourg, Frankrijk
Duur: 22 apr 201826 apr 2018
Congresnummer: 1068903
http://spie.org/conferences-and-exhibitions/photonics-europe?SSO=1

Congres

CongresSPIE Photonics Europe: Neuro-inspired Photonic Computing 2018
LandFrankrijk
StadStrasbourg
Periode22/04/1826/04/18
Internet adres

Vingerafdruk

Multiplexing
multiplexing
Photonics
photonics
Parallel architectures
Lasers
Computing
Phase noise
Phase Noise
Demonstrations
Parallel Architectures
Laser
lasers
readout
Cavity
Simulation
simulation
Laser modes
amplifiers
Photodiodes

Citeer dit

Kassa, W., Dimitriadou, E., Haelterman, M., Massar, S., & Bente, E. (2018). Towards integrated parallel photonic reservoir computing based on frequency multiplexing. In P. Bienstman, & M. Sciamanna (editors), Neuro-inspired Photonic Computing (Vol. 10689). [1068903] SPIE. https://doi.org/10.1117/12.2306176
Kassa, Wosen ; Dimitriadou, Evangelia ; Haelterman, Marc ; Massar, Serge ; Bente, Erwin. / Towards integrated parallel photonic reservoir computing based on frequency multiplexing. Neuro-inspired Photonic Computing. redacteur / Peter Bienstman ; Marc Sciamanna. Vol. 10689 SPIE, 2018.
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Kassa, W, Dimitriadou, E, Haelterman, M, Massar, S & Bente, E 2018, Towards integrated parallel photonic reservoir computing based on frequency multiplexing. in P Bienstman & M Sciamanna (redactie), Neuro-inspired Photonic Computing. vol. 10689, 1068903, SPIE, Strasbourg, Frankrijk, 22/04/18. https://doi.org/10.1117/12.2306176

Towards integrated parallel photonic reservoir computing based on frequency multiplexing. / Kassa, Wosen; Dimitriadou, Evangelia; Haelterman, Marc; Massar, Serge; Bente, Erwin.

Neuro-inspired Photonic Computing. redactie / Peter Bienstman; Marc Sciamanna. Vol. 10689 SPIE, 2018. 1068903.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Kassa W, Dimitriadou E, Haelterman M, Massar S, Bente E. Towards integrated parallel photonic reservoir computing based on frequency multiplexing. In Bienstman P, Sciamanna M, redacteurs, Neuro-inspired Photonic Computing. Vol. 10689. SPIE. 2018. 1068903 https://doi.org/10.1117/12.2306176