Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.Abstract:
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.read more
Citations
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Journal ArticleDOI
Machine learning and the physical sciences
Giuseppe Carleo,J. Ignacio Cirac,Kyle Cranmer,Laurent Daudet,Maria Schuld,Naftali Tishby,Leslie Vogt-Maranto,Lenka Zdeborová +7 more
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Journal ArticleDOI
Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages
Cheng Wang,Cheng Wang,Mian Zhang,Xi Chen,Maxime Bertrand,Maxime Bertrand,Amirhassan Shams-Ansari,Amirhassan Shams-Ansari,Sethumadhavan Chandrasekhar,Peter J. Winzer,Marko Loncar +10 more
TL;DR: Monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels are demonstrated.
Journal ArticleDOI
All-optical machine learning using diffractive deep neural networks
TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI
All-optical spiking neurosynaptic networks with self-learning capabilities.
TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
Journal ArticleDOI
Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures
TL;DR: A tandem neural network architecture is demonstrated that tolerates inconsistent training instances in inverse design of nanophotonic devices and provides a way to train large neural networks for the inverseDesign of complex photonic structures.
References
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A 25 Gb/s Silicon Photonics Platform
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