Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to construct a large number of virtual neurons in the neural network. The clock cycle of the computing network is usually synchronous with the delay time of the feedback loop, which substantially constrains the flexibility of hardware implementations. This work shows an asynchronous reservoir computing network based on a semiconductor laser with an optical feedback loop, where the clock cycle (20 ns) is considerably different to the delay time (77 ns). The performance of this asynchronous network is experimentally investigated under various operation conditions. It is proved that the asynchronous reservoir computing shows highly competitive performance on the prediction task of Santa Fe chaotic time series, in comparison with the synchronous counterparts.
Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry–Perot (FP) semiconductor laser. These modes construct connected physical neurons in parallel, while an optical feedback loop provides interactive virtual neurons in series. We experimentally demonstrate a four-channel wavelength-multiplexing PRC architecture with a total of 80 neurons. The clock rate of the multiplexing PRC reaches as high as 1.0 GHz, which is four times higher than that of the single-channel case. In addition, it is proved that the multiplexing PRC exhibits a superior performance on the task of signal equalization in an optical fiber communication link. This improved performance is owing to the rich neuron interconnections both in parallel and in series. In particular, this scheme is highly scalable owing to the rich mode resources in FP lasers.
This work proposes a deep reservoir computing architecture based on cascading injection-locked quantum dot lasers. It is proved that the four-layer reservoir computing performs better than the single-layer one on multiple benchmark tasks.
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