Photonic reservoir computing is a neuromorphic computing framework which has been successfully used for solving various difficult and time-consuming problems. Due to its photonic nature, it offers many potential advantages such as a low-power consumption and fast processing speed. In this work, we aim to improve an already well-established design of a passive spatially distributed photonic reservoir computer, consisting of a network of waveguides connected via optical splitters and combiners. This spatially distributed architecture1 has shown good performance on a 5-bit header recognition and an isolated spoken digit recognition task. However, this design only incorporates its nonlinearity at the photodiode in its read-out layer and is susceptible to losses within the network. Inspired by the delay-based approach to implement reservoir computing, we opt here for adding extra nonlinearity into the system to increase its nonlinear computational capacity. This is achieved by adding a single semiconductor laser as active component in an external optical delay line: light from the spatial reservoir is injected in a laser, and the optical output of the laser is then fed back to an input port of the spatial reservoir. Based on numerical simulations, we show that the nonlinear computational capacity is significantly increased by adding the feedback loop. This ultimately confirms that adding the active component can be useful for solving more complex tasks.
In photonic reservoir computing, semiconductor lasers with delayed feedback have been used to efficiently solve difficult and time-consuming problems. The injection of data in these systems is often performed optically into the reservoir. Based on simulations, we show that the performance depends heavily on the way that information is encoded in this optical injection signal. In the simulations, we compare various input configurations consisting of Mach-Zehnder modulators and phase modulators for injecting the signal. We observe far better performance in our results, see also [Bauwens et al, Opt. Express 30, 13434 (2022)], on a one-step ahead time-series prediction task when modulating the phase of the injected signal rather than only modulating its amplitude.
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.
Currently, multiple photonic reservoir computing systems show great promise for providing a practical yet powerful hardware substrate for neuromorphic computing. Among those, delay-based systems offer a simple technological route to implement photonic neuromorphic computation. Its operation boils down to a time-multiplexing with the delay length limiting the processing speed. As most optical setups end up to be bulky employing long fiber loops or free-space optics, the processing speeds are ranging from kSa/s to tens of MSa/s. Therefore, we focus on external cavities which are far shorter than what has been realized before in such experiments. We present experimental results of reservoir computing based on a semiconductor laser, operating in a single mode regime around 1550nm, with a 10.8cm delay line. Both are integrated on an active/passive InP photonic chip built on the Jeppix platform. Using 23 virtual nodes spaced 50 ps apart in the integrated delay section, we increase the processing speed to 0.87GSa/s. The computational performance is benchmarked on a forecasting task applied to chaotic time samples. Competitive performance is observed for injection currents above threshold, with higher pumps having lower prediction errors. The feedback strength can be controlled by electrically pumping integrated amplifiers within the delay section. Nevertheless, we find good performance even when these amplifiers are unpumped. To proof the relevance and necessity of the external cavity on the computational capacity, we have analysed linear and nonlinear memory tasks. We also propose several post-processing methods, which increase the performance without a penalty to speed.
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.
Delay-based reservoir computing schemes using semiconductor lasers have proven robustness and good performances for a wide range of tasks. These schemes are especially desirable because of their inherent high speed in data processing and the promise of miniaturization. One such scheme is based on a single-mode semiconductor laser subjected to optical feedback, which can be designed for on-chip implementation. However, the feedback line length remains to be a limiting factor in the miniaturization process. We propose to target more than one mode in a semiconductor lasers. In this way, we believe that it would be possible to distribute the computational power over several modes. Also, having more optical modes addressable will allow for a larger variability and parameter space both at the input and output layers of the reservoir computer. The complex interactions between either optical mode and optical mode or optical mode and carrier densities introduce new dynamical features, as well as increase the available nonlinearity in the system. We envision multiple mode reservoir computing as the next crucial step in optical reservoir computing evolution.
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