Deep neural networks (DNNs) have been successfully applied to solve complex problems, such as pattern recognition when analyzing big data. To achieve a good computational performance, these networks are often designed such that they contain a large number of trainable parameters. However, by doing so, DNNs are often very energy-intensive and time-consuming to train. In this work, we propose to use a photonic reservoir to preprocess the input data instead of directly injecting it into the DNN. A photonic reservoir consists of a network of many randomly connected nodes which do not need to be trained. It forms an additional layer to the deep neural network and can transform the input data into a state in a higher dimensional state-space. This allows us to reduce the size of the DNN, and the amount of training required for the DNN. We test this assumption using numerical simulations that show that such a photonic reservoir as preprocessor results in an improved performance, shown by a lower test error, for a deep neural network, when tested on the one-step ahead prediction task of the Santa Fe time-series. The performance of the stand-alone DNN is poor on this task, resulting in a high test error. As we also discuss in detail in [Bauwens et al, Frontiers in Physics 10, 1051941 (2022)], we conclude that photonic reservoirs are well-suited as physical preprocessors to deep neural networks for tackling time-dependent tasks due to their fast computation times and low-energy consumption.
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.
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