Deep learning with coherent nanophotonic circuits

TL;DR: In this paper , a photonic emulator that uses light propagation instead of electromagnetic simulation is proposed to enable high-precision reconfiguration of the design target on the basis of precise tuning of the effective refractive index near the pixels in the design area and fast feedback of the optical response.

TL;DR: Quantum-enhanced advanced photonic illumination technologies offer unprecedented accuracy in sensing and imaging, secure communications, and computing. The integration of quantum and classical systems enables powerful and flexible platforms and opens new avenues for lighting techniques impossible with conventional optics.

TL;DR: In this article , the topological nonlinear parametric amplification of light in a dimerized coupled waveguide system based on the Su-Schrieffer-Heeger model with a domain wall is demonstrated.

TL;DR: In this paper, a Si microring resonator (MRR) crossbar arrays are used as a programmable nanophotonoic processor (PNP) for a deep learning accelerator.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.