Hardware Realization of Neuromorphic Computing with a 4-Port Photonic Reservoir for Modulation Format Identification
arxiv(2024)
摘要
The fields of machine learning and artificial intelligence drive researchers
to explore energy-efficient, brain-inspired new hardware. Reservoir computing
encompasses recurrent neural networks for sequential data processing and
matches the performance of other recurrent networks with less training and
lower costs. However, traditional software-based neural networks suffer from
high energy consumption due to computational demands and massive data transfer
needs. Photonic reservoir computing overcomes this challenge with
energy-efficient neuromorphic photonic integrated circuits or NeuroPICs. Here,
we introduce a reservoir NeuroPIC used for modulation format identification in
C-band telecommunication network monitoring. It is built on a
silicon-on-insulator platform with a 4-port reservoir architecture consisting
of a set of physical nodes connected via delay lines. We comprehensively
describe the NeuroPIC design and fabrication, experimentally demonstrate its
performance, and compare it with simulations. The NeuroPIC incorporates
non-linearity through a simple digital readout and achieves close to 100
accuracy in identifying several configurations of quadrature amplitude
modulation formats transmitted over 20 km of optical fiber at 32 GBaud symbol
rate. The NeuroPIC performance is robust against fabrication imperfections like
waveguide propagation loss, phase randomization, etc. and delay line length
variations. Furthermore, the experimental results exceeded numerical
simulations, which we attribute to enhanced signal interference in the
experimental NeuroPIC output. Our energy-efficient photonic approach has the
potential for high-speed temporal data processing in a variety of applications.
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