Spiking neural networks are a class of artificial neural networks maintaining a strict analogy to brain-like processing. I’ll show a new hardware approach in which semiconductor microcavities in strong light-matter coupling regime can operate as optical spiking neurons. We demonstrated the intrinsic property of exciton-polaritons to resemble the Leaky Integrate-and-Fire spiking mechanism. Polaritons when pumped with a pulsed laser exhibit leaky-integration due to relaxation of the excitonic reservoir, threshold-and-fire mechanism due to transition to polariton condensate, and resetting due to stimulated emission of photons. Our approach provides means for energy-efficient ultrafast processing of spike-like laser pulses at the level below 1 pJ/spike.
The concept of Neuromorphic Photonics introduces advantages of optical information processing into the neuromorphic engineering domain. Most of the current efforts in the field are focused on identifying the potential mechanisms for useful and flexible spiking neuron implementation. We propose a new approach in which microcavities exhibiting strong exciton-photon interaction may serve as building blocks of optical spiking neurons. Our experiments prove similarities between polariton in-out pulse characteristics and the fundamental spiking behavior of a biological neuron. These effects, evidenced in photoluminescence characteristics, arise within sub-ns timescales. The presented approach provides means for energy-efficient ultrafast processing of spike-like laser pulses.
We show that time-delayed nonlinear effects observed between exciton-polariton condensates can be used to create neural networks in which information is encoded in time. We form condensates on semiconductor microcavity using optical pulses that reach the sample at different times. Strongly nonlinear effects are induced by time-dependent interactions with a long-lived excitonic reservoir. Such nonlinearities make it possible to create a nonlinear XOR logic gate that performs operations with a picosecond time scale. A neural network based on such a logic gate performs a speech recognition tasks with high accuracy.
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