Kavli Affiliate: Alireza Marandi
| First 5 Authors: Midya Parto, Gordon H. Y. Li, Ryoto Sekine, Robert M. Gray, Luis L. Ledezma
| Summary:
Over the past decade, artificial intelligence (AI) has led to disruptive
advancements in fundamental sciences and everyday technologies. Among various
machine learning algorithms, deep neural networks have become instrumental in
revealing complex patterns in large datasets with key applications in computer
vision, natural language processing, and predictive analytics. On-chip photonic
neural networks offer a promising platform that leverage high bandwidths and
low propagation losses associated with optical signals to perform analog
computations for deep learning. However, nanophotonic circuits are yet to
achieve the required linear and nonlinear operations simultaneously in an
all-optical and ultrafast fashion. Here, we report an ultrafast nanophotonic
neuromorphic processor using an optical parametric oscillator (OPO) fabricated
on thin-film lithium niobate (TFLN). The input data is used to modulate the
optical pulses synchronously pumping the OPO. The consequent signal pulses
generated by the OPO are coupled to one another via the nonlinear delayed
dynamics of the OPO, thus forming the internal nodes of a deep recurrent neural
network. We use such a nonlinearly coupled OPO network for chaotic time series
prediction, nonlinear error correction in a noisy communication channel, as
well as noisy waveform classification and achieve accuracies exceeding 93% at
an operating clock rate of ~ 10 GHz. Our OPO network is capable of achieving
sub-nanosecond latencies, a timescale comparable to a single clock cycle in
state-of-the-art digital electronic processors. By circumventing the need for
optical-electronic-optical (OEO) conversions, our ultrafast nanophotonic neural
network paves the way for the next generation of compact all-optical
neuromorphic processors with ultralow latencies and high energy efficiencies.
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