Yue Li Tian

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There has been a recent explosion of interest in spiking neural networks (SNNs), which code information as spikes or events in time. Spike encoding is widely accepted as the information medium underlying the brain, but it has also inspired a new generation of neuromorphic hardware. Although electronics can match biological time scales and exceed them, they(More)
We demonstrate for the first time an excitable laser using graphene. This technology is a potential candidate for applications in novel all-optical devices for information processing and computing. Spike processing has evolved in biological (nervous systems) and engineered (neuromorphic analog VLSI) systems where information is encoded as events in time.(More)
We propose a novel excitable laser employing passively Q-switching with a graphene saturable absorber for spike processing networks. Our approach combines the picosecond processing and switching capabilities of both linear and nonlinear optical device technologies to integrate both analog and digital optical processing into a single hardware architecture(More)
We propose and experimentally demonstrate an optical steganography method in which a data signal is transmitted using amplified spontaneous emission (ASE) noise as a carrier. The ASE serving as a carrier for the private signal has an identical frequency spectrum to the existing noise generated by the Erbium doped fiber amplifiers (EDFAs) in the transmission(More)
We experimentally investigate and characterize the improvement in thresholding capability of a compact highly Ge-doped nonlinear interferometer-based all-optical thresh-older using optical offset spectral filtering. The thresholder we study has an in-loop nonlin-earity requirement lower than that of a classical nonlinear loop mirror scheme. Therefore, only(More)
We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration(More)
Biological neurons perform information processing using a model called pulse processing, which is both computationally efficient and scalable, adopting the best features of both analog and digital computing. Implementing pulse processing with photonics can result in bandwidths that are billions of times faster than biological neurons and substantially(More)
We developed an asynchronous spiking photonic neuron that forms the basic building block for hybrid analog/digital lightwave neuromorphic processing. Our approach enables completely asynchronous spiking in response to input signals while maximizing the throughput relative to synchronous approaches. Asynchronous operation is achieved by generating the spike(More)