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Event-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emit...

Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Cal...

The technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific archite...

Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative p...

Photonic computing and neuromorphic computing are attracting tremendous interests in breaking the memory wall of traditional von Neumann architecture. Photonic memristors equipped with light sensing, data storage, and in...

Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on ...

As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired s...

In this paper we describe a framework to uncover potential causal relations between event mentions from streaming text of news media. This framework relies on a dataset of manually labeled events to train a recurrent neu...