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
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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
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Chalcogenide phase-change devices for neuromorphic photonic computing
Frank Brückerhoff-Plückelmann,Johannes Feldmann,C. David Wright,Harish Bhaskaran,Wolfram H. P. Pernice +4 more
TL;DR: Two prototypes of neuromorphic photonic computation units based on chalcogenide phase-change materials, including a neuromorphic hardware accelerator designed to carry out matrix vector multiplication in convolutional neural networks and an all-optical spiking neuron, which can serve as a building block for large-scale artificial neural networks.
Journal ArticleDOI
Ultra-low loss hybrid ITO/Si thermo-optic phase shifter with optimized power consumption.
TL;DR: The obtained results demonstrate the potential of using ITO as an ultra-low loss microheater for high performance silicon thermo-optic tuning and open an alternative way for enabling the large-scale integration of phase shifters required in emerging integrated photonic applications.
Journal ArticleDOI
Scale-, Shift-, and Rotation-Invariant Diffractive Optical Networks
TL;DR: In this article, the authors focus on developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications, such as optical networks.
Journal ArticleDOI
Freely scalable and reconfigurable optical hardware for deep learning.
TL;DR: In this paper, the authors proposed a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values, which enables information locality between a transmitter and a large number of arbitrarily arranged receivers.
Journal ArticleDOI
Arbitrary linear transformations for photons in the frequency synthetic dimension.
TL;DR: In this article, a photonic architecture is presented to achieve arbitrary linear transformations by harnessing the synthetic frequency dimension of photons, which can be reconfigured to implement a wide variety of manipulations including single-frequency conversion, nonreciprocal frequency translations, and unitary as well as non-unitary transformations.
References
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