Multiple photonic systems show great promise for providing practical yet powerful hardware substrates for neuromorphic computing. Among those, delay-based systems offer -through a time-multiplexing technique - a simple technological implementation route. We discuss our advances in the development of passive coherent fibre-ring cavities and semiconductor lasers with integrated delay for reservoir computing. Time-multiplexed systems are also highly suitable for coherent Ising machines as they allow to implement a fully interconnected large scale system with few components. We have recently proposed a system based on opto-electronic oscillators subjected to self-feedback with improved calculation time and solution quality.
We exploit the transient dynamics of a nonlinear photonic system to perform useful computation. This is achieved within the framework of reservoir computing. State of the art implementations in photonic hardware are evolving towards simple architectures. With nonlinearities present in either the reservoirs input or output layer, even a linear photonic cavity makes for a potent reservoir. However, when targeting all-optical reservoir computers (coming from opto-electronic systems), commonly used non-linearities in opto-electronic conversion equipment, such as modulators and photodiodes, can no longer be relied on. Therefore, optical nonlinearities must be considered. In this work, we numerically and experimentally investigate a delay-based reservoir implemented in standard single mode optical fibers. Our setup is coherently driven and exploits the optical Kerr nonlinearity, which is present throughout the reservoir’s extent (i.e. the fiber ring cavity), to operate as a state-of-the-art photonic reservoir. A set of systems was considered, with different combinations of linear and nonlinear input and output schemes. And we have been able to quantify the effects of different nonlinearities in the system on its reservoir computing performance. Experimental data shows the positive effects of the distributed Kerr nonlinearity on both the linear memory capacity and nonlinear computational capacity of our reservoir computing system. We find a broad range of power levels where this distributed nonlinear effect improves the reservoirs performance. Moreover, we find that the exploitation of this optical nonlinearity in the reservoirs bulk allows for state-of-the-art reservoir computing performance without relying on opto-electronic nonlinearities elsewhere in the system.
Reservoir computing (RC) has reinvigorated neuromorphic computing activities in photonics. RC radically reduces the required complexity for a hardware implementation in photonics as compared to earlier efforts in the nineties. Currently, multiple photonic RC systems show great promise for providing a practical yet powerful hardware substrate for neuromorphic computing. Among those, delay-based systems offer through a time-multiplexing technique a simple technological route to implement photonic neuromorphic computation. We will review the state of the art on delay-based RC and discuss our advances in substrates implemented as passive coherent fibre-ring cavities and semiconductor lasers with delayed optical feedback. Passive coherent reservoirs built using fiber loops have achieved record performances, but are still aided by nonlinear electro-optical transformations at the input and output. Nevertheless, when targeting all-optical reservoirs, these nonlinearities will be absent. We have found that optical nonlinearities in the fibre itself can be sufficient to enhance the task solving capabilities of a passive reservoir. Also, delay-based optical substrates for RC tend to be quite bulky employing long fiber loops or free-space optics. As a result, the processing speeds are limited in the range of kSa/s to tens of MSa/s. We have studied and developed substrates using external cavities which are far shorter than what has been realized before in experiment. Specifically, by integrating a semiconductor laser together with a 10.8 cm delay line on an active/passive InP photonic chip using the Jeppix platform, we can increase the processing speed to GSa/s.
We present a novel speckle reduction scheme for application in laser-based projection systems. The scheme combines the use of a microlens array (MLA) as screen material with the concept of reduced spatial coherence. Incorporating the screen in the speckle reduction process reduces laser projector cost and complexity. On a typical screen, random scattering of coherent light would cause random interference, i.e. speckle. On an MLA screen however, the interference between the fields emitted by different microlenses is inhibited if the spatial coherence area of the incident light is made smaller than the microlens footprint. We tested both a MLA with randomly arranged lenses of varying size, averaging 120 μm in diameter, and a MLA with regularly spaced lenses with a fixed diameter of 100 μm. We benchmarked the performance of these MLA screens and a regular diffusive screen. Using a small-scale projection setup with a CCD camera as observer, we experimentally quantified the speckle contrast observed on these screens. Objective speckle contrast measurements on the irregular MLA yield results close to the subjective human speckle detection limit. Besides the experimental validation of the proposed speckle reduction scheme, we constructed a quantitative model to describe the speckle characteristics of the different screens. The model corresponds very well with experimental results and allows us to quantify the relative contributions of the different speckle reduction processes at play. Our approach can benefit any laser-based projection system, such as for example 3D cinema.
We present numerical results on a spatially parallel photonic reservoir computer. In this computing paradigm, an input signal couples to a randomly interconnected reservoir of state variables (neurons). The reservoirs output is constructed by combining the neural responses with different weights, and is used to perform useful computation. Reservoir computers are easy to train as only these output weights are optimize while keeping internal connections fixed. We are currently building a bulk optics high bandwidth reservoir computer where neurons are encoded using the spatial degree of freedom of light. We use a linear Fabry-Prot resonator as reservoir and implement a nonlinear readout layer. New input samples are injected every 2ns. The neurons are encoded as a grid of 9 by 9 spots in the 2-dimensional transverse spatial extent of the cavity input coupler. We place a lens in the middle of the resonator with focal length half the resonator length, so that the conjugate plane of the neuron grid is on the resonator back plane. At this end, a phase-only spatial light modulator acts as a programmable diffraction grating, mixing the spatial modes in the resonator. We have simulated the optical reservoir and an electronic nonlinear output layer. These simulations were performed in discrete time, and take into account photodetector noise. We study the effect of the diffractive coupling scheme and its symmetry on the simulated reservoir computing performance on a standard benchmark test. We find that symmetry improve noise robustness at the expense of diversity in the neural responses.
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