Browsing by Author "Freiberger, Matthias"

Now showing 1 - 10 of 10
  • Thumbnail Image
    Item
    All-Optical Reservoir Computing on a Photonic Chip Using Silicon-Based Ring Resonators
    (IEEE Journal of Selected Topics in Quantum Electronics, 2018) Denis-Le Coarer, Florian; Sciamanna, Marc; Katumba, Andrew; Freiberger, Matthias; Dambre, Joni; Bienstman, Peter; Rontani, Damien
    We present in our work numerical results on the performance of a 4 × 4 swirl-topology photonic reservoir integrated on a silicon chip. Nonlinearmicroring resonators are used as nodes. We analyze the performance of such a reservoir on a classical nonlinear Boolean task (the delayed XOR task) for: various designs of the reservoir in terms of lengths of the waveguides between consecutive nodes, and various injection parameters (injected power and optical detuning). From this analysis, we find that this kind of reservoir can perform–for a large variety of parameters–the delayed XOR task at 20 Gb/s with bit error rates lower than 10−3 and an averaged injection power lower than 2.5 mW.
  • Thumbnail Image
    Item
    Improving Time Series Recognition and Prediction with Networks and Ensembles of Passive Photonic Reservoirs
    (IEEE Journal of Selected Topics in Quantum Electronics, 2019) Freiberger, Matthias; Sackesyn, Stijn; Ma, Chonghuai; Katumba, Andrew; Bienstman, Peter; Dambre, Joni
    As the performance increase of traditional Von- Neumann computing attenuates, new approaches to computing need to be found. A promising approach for low-power computing at high bitrates is integrated photonic reservoir computing. In the past though, the feasible reservoir size and computational power of integrated photonic reservoirs have been limited by hardware constraints. An alternative solution to building larger reservoirs is the combination of several small reservoirs to match or exceed the performance of a single bigger one. This work summarizes our efforts to increase the available computational power by combining multiple reservoirs into a single computing architecture. We investigate several possible combination techniques and evaluate their performance using the classic XOR and header recognition tasks as well as the well-known Santa Fe chaotic laser prediction task. Our findings suggest that a new paradigm of feeding a reservoir’s output into the readout structure of the next one shows consistently good results for various tasks as well as for both electrical and optical readouts and coupling schemes.
  • Thumbnail Image
    Item
    A Multiple-Input Strategy to Efficient Integrated Photonic Reservoir Computing
    (Cognitive Computation,, 2017) Katumba, Andrew; Freiberger, Matthias; Bienstman, Peter; Dambre, Joni
    Photonic reservoir computing has evolved into a viable contender for the next generation of analog computing platforms as industry looks beyond standard transistor-based computing architectures. Integrated photonics reservoir computing, particularly on the Silicon-on-Insulator platform, presents a CMOS-compatible, wide-bandwidth, parallel platform for implementation of optical reservoirs. A number of demonstrations of the applicability of this platform for processing optical telecommunications signals have been made in the recent past. In this work, we take it a stage further by performing an architectural search for designs that yield the best performance while maintaining power efficiency.
  • Thumbnail Image
    Item
    Neuromorphic computing based on silicon photonics
    (IEEE Journal of Selected Topics in Quantum Electronics, 2018) Katumba, Andrew; Freiberger, Matthias; Laporte, Floris; Lugnan, Alessio; Sackesyn, Stijn; Ma, Chonghuai; Dambre, Joni; Bienstman, Peter
    We present our latest progress using new neuromorphic paradigms for optical information processing in silicon photonics. We show how passive reservoir computing chips can be used to perform a variety of tasks (bit level tasks, nonlinear dispersion compensation, ...) at high speeds and low power consumption. In addition, we present a spatial analog of reservoir computing based on pillar scatterers and a cavity, that can be used to speed up classification of biological cells.
  • Thumbnail Image
    Item
    Neuromorphic information processing using silicon photonics
    (IEEE Journal of Selected Topics in Quantum Electronics, 2018) Bienstman, Peter; Dambre, Joni; Katumba, Andrew; Freiberger, Matthias; Laporte, Floris
    We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. First, we dicuss how passive reservoir computing can be used to perform non-linear signal equalisation in telecom links. Then, we introduce a training method that can deal with limited weight resolution for a hardware implementation of a photonic readout.
  • Thumbnail Image
    Item
    On-chip Passive Photonic Reservoir Computing with Integrated Optical Readout
    (IEEE International Conference on Rebooting Computing, 2017) Freiberger, Matthias; Katumba, Andrew; Bienstman, Peter; Dambre, Joni
    Photonic reservoir computing is a recent bioinspired paradigm for signal processing. Despite first successes, the paradigm still faces challenges. We address some of these challenges and introduce our approaches to solve them. In detail, we discuss how integrated reservoirs can be scaled up by injecting multiple copies of the input. Further we introduce a new hardware-friendly training method for integrated optical readouts.
  • Thumbnail Image
    Item
    Reservoir Computing with Nonlinear Micro-Resonators on a Silicon Photonics Chip
    (NOLTA, 2017) Rontani, Damien; Katumba, Andrew; Freiberger, Matthias; Dambre, Joni; Bienstman, Peter; Sciamanna, Marc
    We present here recent advances in the use of a small network of nonlinear micro-resonators integrated on a Silicon chip as a reservoir computer. We provide numerical evidence that this novel photonic integrated circuit can perform binary-type tasks (e.g.: the XOR task or multi-bit header recognition task) at bitrate of 20 Gb/s with a performance level adequate for telecom applications. We analyze the impact of key operational parameters (e.g.: optical power injected) and topological properties of the network on the level of performance of the proposed architecture. Finally, we will compare the performance between this new chip with a previous generation of passive reservoir [1] realized with splitters and combiners without any internal nonlinearity.
  • Thumbnail Image
    Item
    Silicon photonics for neuromorphic information processing
    (SPIE, 2018) Bienstman, Peter; Dambre, Joni; Katumba, Andrew; Freiberger, Matthias; Laporte, Floris; Lugnan, Alessio
    We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power e ciency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.
  • Thumbnail Image
    Item
    Toward neuro-inspired computing using a small network of micro-ring resonators on an integrated photonic chip
    (SPIE, 2018) Denis-le Coarera, Florian; Rontania, Damien; Katumba, Andrew; Freiberger, Matthias; Dambre, Joni; Bienstman, Peter; Sciamanna, Marc
    We present in this work numerical simulations of the performance of an on-chip photonic reservoir computer using nonlinear microring resonator as neurons. We present dynamical properties of the nonlinear node and the reservoir computer, and we analyse the performance of the reservoir on a typical nonlinear Boolean task : the delayed XOR task. We study the performance for various designs (number of nodes, and length of the synapses in the reservoir), and with respect to the properties of the optical injection of the data (optical detuning and power). From this work, we nd that such a reservoir has state-of-the art level of performance on this particular task - that is a bit error rate of 2.5 10􀀀4 - at 20 Gb/s, with very good power e ciency (total injected power lower than 1.0 mW).
  • Thumbnail Image
    Item
    Training Passive Photonic Reservoirs with Integrated Optical Readout
    (IEEE, 2018) Freiberger, Matthias; Katumba, Andrew; Bienstman, Peter; Dambre, Joni
    As Moore’s law comes to an end, neuromorphic approaches to computing are on the rise. One of these, passive photonic reservoir computing, is a strong candidate for computing at high bitrates (>10 Gbps) and with low energy consumption. Currently though, both benefits are limited by the necessity to perform training and readout operations in the electrical domain. Thus, efforts are currently underway in the photonic community to design an integrated optical readout, which allows to perform all operations in the optical domain. In addition to the technological challenge of designing such a readout, new algorithms have to be designed in order to train it. Foremost, suitable algorithms need to be able to deal with the fact that the actual on-chip reservoir states are not directly observable. In this work, we investigate several options for such a training algorithm and propose a solution in which the complex states of the reservoir can be observed by appropriately setting the readout weights, while iterating over a predefined input sequence. We perform numerical simulations in order to compare our method with an ideal baseline requiring full observability as well as with an established black-box optimization approach (CMA-ES).