Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-12404
 Main Title: A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser Author(s): Porte, XavierSkalli, AnasHaghighi, NasibehReitzenstein, StephanLott, James A.Brunner, Daniel Type: Article URI: https://depositonce.tu-berlin.de/handle/11303/13617http://dx.doi.org/10.14279/depositonce-12404 License: https://creativecommons.org/licenses/by/4.0/ Abstract: Neural 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. Subject(s): photonic neural networkscomplex photonicsvertical-cavity surface-emitting lasers Issue Date: 29-Apr-2021 Date Available: 20-Sep-2021 Language Code: en DDC Class: 530 Physik Sponsor/Funder: DFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, BauelementeEC/H2020/713694/EU/International Mobility and Training in Photonics Programme/MULTIPLY Journal Title: JPhys Photonics Publisher: IOP Volume: 3 Issue: 2 Article Number: 024017 Publisher DOI: 10.1088/2515-7647/abf6bd EISSN: 2515-7647 TU Affiliation(s): Fak. 2 Mathematik und Naturwissenschaften » Inst. Festkörperphysik » AG Optoelektronik und Quantenbauelemente Appears in Collections: Technische Universität Berlin » Publications