A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser

dc.contributor.authorPorte, Xavier
dc.contributor.authorSkalli, Anas
dc.contributor.authorHaghighi, Nasibeh
dc.contributor.authorReitzenstein, Stephan
dc.contributor.authorLott, James A.
dc.contributor.authorBrunner, Daniel
dc.date.accessioned2021-09-20T07:46:25Z
dc.date.available2021-09-20T07:46:25Z
dc.date.issued2021-04-29
dc.description.abstractNeural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for parallelism across the entire processor and for a co-location of memory and arithmetic, i.e. beyond von Neumann architectures. Parallelism in particular made photonics a highly promising platform, yet until now scalable and integratable concepts are scarce. Here, we demonstrate for the first time how a fully parallel and fully implemented photonic neural network can be realized by spatially multiplexing neurons across the complex optical near-field of a semiconductor multimode laser. Discrete spatial sampling defines ∼90 nodes on the surface of a large-area vertical cavity surface emitting laser that is optically injected with the artificial neural networks input information. Importantly, all neural network connections are realized in hardware, and our processor produces results without pre- or post-processing. Input and output weights are realized via the complex transmission matrix of a multimode fiber and a digital micro-mirror array, respectively. We train the readout weights to perform 2-bit header recognition, a 2-bit XOR logical function and 2-bit digital to analog conversion, and obtain $\lt0.9 \times 10^{-3}$ and 2.9 × 10−2 error rates for digit recognition and XOR, respectively. Finally, the digital to analog conversion can be realized with a standard deviation of only 5.4 × 10−2. Crucially, our proof-of-concept system is scalable to much larger sizes and to bandwidths in excess of 20 GHz.en
dc.description.sponsorshipDFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, Bauelementeen
dc.description.sponsorshipEC/H2020/713694/EU/International Mobility and Training in Photonics Programme/MULTIPLYen
dc.identifier.eissn2515-7647
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13617
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12404
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc530 Physikde
dc.subject.otherphotonic neural networksen
dc.subject.othercomplex photonicsen
dc.subject.othervertical-cavity surface-emitting lasersen
dc.titleA complete, parallel and autonomous photonic neural network in a semiconductor multimode laseren
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber024017en
dcterms.bibliographicCitation.doi10.1088/2515-7647/abf6bden
dcterms.bibliographicCitation.issue2en
dcterms.bibliographicCitation.journaltitleJPhys Photonicsen
dcterms.bibliographicCitation.originalpublishernameIOPen
dcterms.bibliographicCitation.originalpublisherplaceBristolen
dcterms.bibliographicCitation.volume3en
tub.accessrights.dnbfreeen
tub.affiliationFak. 2 Mathematik und Naturwissenschaften>Inst. Festkörperphysik>AG Optoelektronik und Quantenbauelementede
tub.affiliation.facultyFak. 2 Mathematik und Naturwissenschaftende
tub.affiliation.groupAG Optoelektronik und Quantenbauelementede
tub.affiliation.instituteInst. Festkörperphysikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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