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High-speed parallel processing with photonic feedforward reservoir computing
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DOI：10.1364/OE.505520.png)
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High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time -delayed feedback loops and use analog -to -digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non -feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback -laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully -analog feed -forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength -division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keyword:
LARGE-SCALE
DYNAMICS
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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